├── run_extract_feats.sh
├── run_fewshot_baseline.sh
├── baseline
    ├── compute_metric.py
    ├── fewshot_baseline.py
    ├── extract_feats.py
    ├── baseline_func.py
    ├── metabaseline_func.py
    └── gen_support_set.py
├── README.md
├── data
    ├── NeoJaundice
    │   ├── fewshot-pool.txt
    │   └── test.txt
    ├── Retino
    │   ├── fewshot-pool.txt
    │   └── test.txt
    └── Endo
    │   └── fewshot-pool.txt
└── LICENSE


/run_extract_feats.sh:
--------------------------------------------------------------------------------
1 | ### sample script of extract features of test set in MedFMC
2 | python ./baseline/extract_feats.py --model simmim-swin-base --dataset ChestDR --data_dir ./data/ChestDR
3 | python ./baseline/extract_feats.py --model swin-base --dataset ChestDR --data_dir ./data/ChestDR
4 | 


--------------------------------------------------------------------------------
/run_fewshot_baseline.sh:
--------------------------------------------------------------------------------
1 | ### sample script of run fewshot baseline on test set of MedFMC
2 | python ./baseline/fewshot_baseline.py --method Baseline --model swin-base --dataset ChestDR --data_dir ./data/ChestDR --shot 10 --max_iters 10 --job_name test_shot10_base
3 | python ./baseline/fewshot_baseline.py --method MetaBaseline --model swin-base --dataset ChestDR --data_dir ./data/ChestDR --shot 10 --max_iters 10 --job_name test_meta_shot10_base
4 | 


--------------------------------------------------------------------------------
/baseline/compute_metric.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | import numpy as np
 3 | import torch
 4 | from sklearn import metrics
 5 | from mmpretrain.evaluation import AveragePrecision
 6 | 
 7 | ### 1. compute auc for each class
 8 | def compute_auc(cls_scores, cls_labels):
 9 |     cls_aucs = []
10 |     for i in range(cls_scores.shape[1]):
11 |         scores_per_class = cls_scores[:, i]
12 |         labels_per_class = cls_labels[:, i]
13 |         auc_per_class = metrics.roc_auc_score(labels_per_class, scores_per_class)
14 |         # print ('class {} auc = {:.2f}'.format(i+1, auc_per_class*100))
15 |         
16 |         cls_aucs.append(auc_per_class)
17 |     
18 |     return cls_aucs   
19 | 
20 | ### 2. compute AP for each class
21 | def compute_ap(cls_scores, cls_labels):
22 |     cls_aps = []
23 |     for i in range(cls_scores.shape[1]):
24 |         scores_per_class = cls_scores[:, i]
25 |         labels_per_class = cls_labels[:, i]
26 | 
27 |         scores_per_class = scores_per_class.reshape(cls_scores.shape[0], 1)
28 |         labels_per_class = labels_per_class.reshape(cls_scores.shape[0], 1)
29 | 
30 |         # print (scores_per_class.shape, labels_per_class.shape)
31 | 
32 |         ap = AveragePrecision.calculate(scores_per_class, labels_per_class)
33 |         ap_per_class = ap.detach().numpy()
34 | 
35 |         cls_aps.append(ap_per_class/100)
36 |     
37 |     return cls_aps
38 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # MedFMC_fewshot_baseline
 2 | Implementation of two few-shot method (Baseline and Meta Baseline) for MedFMC
 3 | ****
 4 | ## Requirements
 5 | * The following setup has been tested on Python 3.9, Ubuntu 20.04.  
 6 | * mmpretrain (recommended 1.0.0rc8): please refer to https://github.com/open-mmlab/mmpretrain for installation details.     
 7 | * sklearn (recommended 1.2.2): pip install sklearn  
 8 | ****
 9 | ## Usage 
10 | * Run the script of '**run_extract_feats.sh**' to extract the features via pretrained models (e.g. swin-base) of all the test images in each dataset (ChestDR, Endo, NeoJaudice, ColonPath, Retino). The extracted features would be stored as '.npy' format file in the sub-folder 'test_feats/' of the dataset path (e.g. ./data/ChestDR/test_feats/swin-base.npy).   
11 | * Run the script of '**run_fewshot_baseline.sh**' to test the results of Baseline and Meta Baseline method using 1, 5, 10 shot samples per class under 10 iterations for each dataset. 
12 | ****
13 | ## Dataset Split
14 | * In our experiments, image list used for randomly picking support set of each dataset is saved as 'fewshot-pool.txt', meanwhile image list consisted of the remaining testing images as 'test.txt'. These two image list files can also be found in data folder (e.g. ./data/ChestDR/test.txt).
15 | * When you start to test this baseline, please place all the preprocessed images of each dataset into the sub-folder 'images/' beforehand (e.g. ./data/ChestDR/images/).   
16 | ****
17 | ## Cite this article
18 | Wang, D., Wang, X., Wang, L. et al. A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification. Sci Data 10, 574 (2023). https://doi.org/10.1038/s41597-023-02460-0
19 | 


--------------------------------------------------------------------------------
/baseline/fewshot_baseline.py:
--------------------------------------------------------------------------------
 1 | import os
 2 | import torch
 3 | import numpy as np
 4 | import argparse
 5 | 
 6 | from mmpretrain import get_model
 7 | from extract_feats import extract_model_fea, load_annotations, load_endo_annotations, load_chest_annotations, make_dir
 8 | from gen_support_set import gen_support_set_interface, save_fewshot_data
 9 | from baseline_func import do_baseline
10 | from metabaseline_func import do_metabaseline
11 | 
12 | DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
13 | MedFMC_CLS_NUM = {'ColonPath': 2, 'NeoJaundice': 2, 'Retino': 5, 'Endo': 4, 'ChestDR': 19}
14 | 
15 | 
16 | def parse_args():
17 |     parser = argparse.ArgumentParser(description='run few shot baseline on MedFMC')
18 |     parser.add_argument('--method', default=None)                       ### Baseline / MetaBaseline
19 |     parser.add_argument('--model', default=None)                        ### swin-base / simmim-swin-base    
20 |     parser.add_argument('--dataset', default=None)                      ### Retino / ColonPath / NeoJaundice / Endo / ChestDR   
21 |     parser.add_argument('--data_dir', default=None)
22 |     parser.add_argument('--shot', default=1, type=int)                  ### 1, 5, 10
23 |     parser.add_argument('--max_iters', default=1, type=int)
24 |     parser.add_argument('--job_name', default=None)
25 | 
26 |     args = parser.parse_args()
27 |     
28 |     return args
29 | 
30 | 
31 | def run():
32 |     args = parse_args()  
33 |     print(args)
34 | 
35 |     ### step 1. load model
36 |     model_config = None
37 |     fea_dim = 0
38 |     if args.model == 'swin-base':
39 |         model_config = 'swin-base_16xb64_in1k'
40 |         fea_dim = 1024
41 |     elif args.model == 'simmim-swin-base':
42 |         model_config = 'swin-base-w6_simmim-800e-pre_8xb256-coslr-100e_in1k-192px'
43 |         fea_dim = 1024
44 |     model = get_model(model_config, pretrained=True, device=DEVICE)
45 | 
46 |     ### step 2. load img list
47 |     train_list_txt = os.path.join(args.data_dir, 'fewshot-pool.txt')
48 |     test_list_txt = os.path.join(args.data_dir, 'test.txt')
49 | 
50 |     train_img_infos = []
51 |     test_img_infos = []
52 |     if args.dataset == 'ChestDR':
53 |         train_img_infos = load_chest_annotations(train_list_txt)
54 |         test_img_infos = load_chest_annotations(test_list_txt)
55 |     elif args.dataset == 'Endo':
56 |         train_img_infos = load_endo_annotations(train_list_txt)
57 |         test_img_infos = load_endo_annotations(test_list_txt)
58 |     else:
59 |         train_img_infos = load_annotations(train_list_txt)
60 |         test_img_infos = load_annotations(test_list_txt)
61 | 
62 |     N_way = MedFMC_CLS_NUM[args.dataset]
63 |     K_shot = args.shot
64 | 
65 |     print ('N_way = ', N_way, ', K_shot = ', K_shot)
66 | 
67 | 
68 |     all_support_sets = []
69 |     all_test_results = []
70 | 
71 |     for iter in range(args.max_iters):
72 |         print ('\n----------------test iter = {}----------------'.format(iter))
73 |         ### step 3. support set randomly sampled from the fewshot-pool set
74 |         support_set = gen_support_set_interface(train_img_infos, N_way, K_shot, args.dataset)
75 |         all_support_sets.append(support_set)
76 | 
77 |         ### step 4. run test result and compute metrics   
78 |         one_test_res = None
79 |         if args.method == 'Baseline':
80 |             one_test_res = do_baseline(support_set, test_img_infos, args.data_dir, model, args.model, fea_dim, N_way, args.dataset)
81 |         else:
82 |             one_test_res = do_metabaseline(support_set, test_img_infos, args.data_dir, model, args.model, fea_dim, N_way, args.dataset)
83 |         all_test_results.append(one_test_res)
84 | 
85 |     ### step 5. save support list and test result
86 |     job_dir = os.path.join(args.data_dir, 'work_dir/'+args.job_name)
87 |     make_dir(job_dir)
88 |     save_fewshot_data(job_dir, args.dataset, all_support_sets, all_test_results)
89 | 
90 | 
91 | if __name__ == '__main__':
92 |     print ('let us start fewshot baseline test now ...')
93 |     run()
94 |     print ('\nfinish ...')   
95 | 


--------------------------------------------------------------------------------
/baseline/extract_feats.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import torch
  3 | import numpy as np
  4 | from mmpretrain import get_model, FeatureExtractor
  5 | import argparse
  6 | 
  7 | DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
  8 | 
  9 | def make_dir(dir):
 10 |     if not os.path.exists(dir):
 11 |         os.makedirs(dir)
 12 | 
 13 | ### extract norm feat via model for each image 
 14 | def extract_model_fea(model, img_file):
 15 |     inferencer = FeatureExtractor(model, device=DEVICE)
 16 |     feat = inferencer(img_file, stage='pre_logits')[0].cpu()
 17 |     feat = feat / feat.norm()        
 18 |     out_feat = feat.detach().numpy()
 19 |     return out_feat
 20 | 
 21 | def load_annotations(ann_file):    
 22 |     data_infos = []
 23 |     with open(ann_file) as f:
 24 |         samples = [x.strip() for x in f.readlines()]
 25 |         for item in samples:
 26 |             filename = item[:-2]
 27 |             imglabel = int(item[-1:])            
 28 |             info = {}
 29 |             info['filename'] = filename
 30 |             info['gt_label'] = imglabel
 31 |             data_infos.append(info)
 32 |     
 33 |     return data_infos
 34 | 
 35 | def load_endo_annotations(ann_file):    
 36 |     data_infos = []
 37 |     with open(ann_file) as f:
 38 |         samples = [x.strip() for x in f.readlines()]
 39 |         for item in samples:
 40 |             filename = item[:-8]
 41 |             imglabel = item[-7:]
 42 |             gt_label = np.asarray(list(map(int, imglabel.split(' '))), dtype=np.int8)
 43 |             info = {}
 44 |             info['filename'] = filename
 45 |             info['gt_label'] = gt_label
 46 |             
 47 |             data_infos.append(info)
 48 |             
 49 |     return data_infos
 50 | 
 51 | def load_chest_annotations(ann_file):    
 52 |     data_infos = []
 53 |     with open(ann_file) as f:
 54 |         samples = [x.strip() for x in f.readlines()]
 55 |         for item in samples:
 56 |             filename, imglabel = item.split(' ')
 57 |             gt_label = np.asarray(list(map(int, imglabel.split(','))), dtype=np.int8)
 58 |             info = {}
 59 |             info['filename'] = filename
 60 |             info['gt_label'] = gt_label
 61 |             data_infos.append(info)
 62 |             
 63 |     return data_infos
 64 | 
 65 | def extract_test_feats(img_list, model, data_dir, fea_dim):
 66 |     img_num = len(img_list)
 67 | 
 68 |     test_feats = np.zeros((img_num, fea_dim))
 69 | 
 70 |     count = 0
 71 |     for item in img_list:
 72 |         count += 1
 73 |         print ('\r', 'process: {}/{}'.format(count, img_num), end='', flush=True)
 74 |         img_name = item['filename']
 75 |         img_file = os.path.join(data_dir, 'images/', img_name)
 76 | 
 77 |         ###add png or jpg 20231005
 78 |         img_file = img_file + '.jpg'
 79 |         if not os.path.exists(img_file):
 80 |             img_file = img_file.replace('.jpg', '.png')
 81 |         
 82 |         img_fea = extract_model_fea(model, img_file)
 83 |         test_feats[count-1, :] = img_fea
 84 |         # print (img_fea)     
 85 | 
 86 |     return test_feats
 87 | 
 88 | def parse_args():
 89 |     parser = argparse.ArgumentParser(description='extract feats for test set in MedFMC')
 90 |     parser.add_argument('--model', default=None, )                          ### swin-base / simmim-swin-base    
 91 |     parser.add_argument('--dataset', default=None)                          ### Retino / ColonPath / NeoJaundice / Endo / ChestDR   
 92 |     parser.add_argument('--data_dir', default=None)
 93 | 
 94 |     args = parser.parse_args()
 95 |     
 96 |     return args
 97 | 
 98 | def run():
 99 |     args = parse_args()  
100 |     print(args)
101 | 
102 |     model_config = None
103 |     fea_dim = 0
104 |     if args.model == 'swin-base':
105 |         model_config = 'swin-base_16xb64_in1k'
106 |         fea_dim = 1024
107 |     elif args.model == 'simmim-swin-base':
108 |         model_config = 'swin-base-w6_simmim-800e-pre_8xb256-coslr-100e_in1k-192px'
109 |         fea_dim = 1024
110 | 
111 |     model = get_model(model_config, pretrained=True, device=DEVICE)
112 | 
113 |     test_list_txt = os.path.join(args.data_dir, 'test.txt')
114 | 
115 |     test_img_infos = []
116 |     if args.dataset == 'ChestDR':
117 |         test_img_infos = load_chest_annotations(test_list_txt)
118 |     elif args.dataset == 'Endo':
119 |         test_img_infos = load_endo_annotations(test_list_txt)
120 |     else:
121 |         test_img_infos = load_annotations(test_list_txt)
122 |     
123 |     test_img_num = len(test_img_infos)
124 | 
125 |     if model is not None and test_img_num > 0:
126 |         test_feats = extract_test_feats(test_img_infos, model, args.data_dir, fea_dim)
127 |         ### save test feats
128 |         save_dir = os.path.join(args.data_dir, 'test_feats/')
129 |         make_dir(save_dir)
130 |         np.save(os.path.join(save_dir, args.model + '.npy'), test_feats)
131 | 
132 | if __name__ == '__main__':
133 |     print ('let us start extract_test_feats now ...')
134 |     run()
135 |     print ('\nfinish ...')   
136 | 


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/baseline/baseline_func.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import numpy as np
  3 | from extract_feats import extract_model_fea
  4 | from sklearn.linear_model import LogisticRegression
  5 | from sklearn.multioutput import MultiOutputClassifier
  6 | from sklearn import metrics
  7 | from compute_metric import compute_auc, compute_ap
  8 | 
  9 | 
 10 | def train_cls_model_multiclass(support_set, data_dir, model, fea_dim):
 11 |     sample_num = 0
 12 |     for cls_set in support_set:
 13 |         sample_num += len(cls_set)
 14 | 
 15 |     X_train = np.zeros((sample_num, fea_dim))
 16 |     cls_num = len(support_set)
 17 | 
 18 |     print ('sample_num = ', sample_num, ', cls_num = ', cls_num)
 19 | 
 20 |     sample_idx = 0
 21 |     Y_train = []
 22 |     for cls_idx in range(cls_num):
 23 |         imgs_per_cls = support_set[cls_idx]
 24 |         for item in imgs_per_cls:
 25 |             img_file = os.path.join(data_dir, 'images/', item)
 26 |             
 27 |             ###add png or jpg 20231005
 28 |             img_file = img_file + '.jpg'
 29 |             if not os.path.exists(img_file):
 30 |                 img_file = img_file.replace('.jpg', '.png')
 31 |                 
 32 |             img_fea = extract_model_fea(model, img_file)
 33 |             X_train[sample_idx, :] = img_fea
 34 |             sample_idx += 1
 35 |             Y_train.append(cls_idx)
 36 | 
 37 |     print (X_train.shape, len(Y_train))
 38 | 
 39 |     clf = LogisticRegression(random_state=0).fit(X_train, Y_train)
 40 |   
 41 |     return clf
 42 | 
 43 | def train_cls_model_multilabel(support_set, data_dir, model, fea_dim):
 44 |     sample_num = 0
 45 |     for cls_set in support_set:
 46 |         sample_num += len(cls_set)
 47 | 
 48 |     print ('sample_num = ', sample_num)
 49 | 
 50 |     X_train = np.zeros((sample_num, fea_dim))
 51 |     cls_num = len(support_set)
 52 |     sample_idx = 0
 53 |     Y_train = []
 54 |     for cls_idx in range(cls_num):
 55 |         imgs_per_cls = support_set[cls_idx]
 56 |         for item in imgs_per_cls:
 57 |             img_file = os.path.join(data_dir, 'images/', item[0])
 58 | 
 59 |             ###add png or jpg 20231005
 60 |             img_file = img_file + '.jpg'
 61 |             if not os.path.exists(img_file):
 62 |                 img_file = img_file.replace('.jpg', '.png')
 63 |             
 64 |             img_fea = extract_model_fea(model, img_file)
 65 |             X_train[sample_idx, :] = img_fea
 66 |             sample_idx += 1
 67 |             Y_train.append(item[1])
 68 | 
 69 |     clf = MultiOutputClassifier(LogisticRegression()).fit(X_train, Y_train)
 70 | 
 71 |     return clf
 72 | 
 73 | def do_test_multiclass(query_set, data_dir, model_name, clf):
 74 |     print('run do_test_multiclass ...')
 75 | 
 76 |     sample_num = len(query_set)
 77 |     print ('sample_num = ', sample_num)
 78 | 
 79 |     ### load test feats
 80 |     X_Test = np.load(os.path.join(data_dir, 'test_feats/', model_name + '.npy'))
 81 |     print (X_Test.shape)
 82 |     
 83 |     Y_test = []
 84 |     sample_idx = 0
 85 |     for item in query_set:
 86 |         img_label = item['gt_label']
 87 |         Y_test.append(img_label)
 88 | 
 89 |     prob_Test = clf.predict_proba(X_Test)
 90 |     print (prob_Test.shape, prob_Test[0])
 91 | 
 92 |     pred_labels = []
 93 |     for i in range(sample_num):
 94 |         sample_prob = prob_Test[i]
 95 |         label = np.argmax(sample_prob)
 96 |         pred_labels.append(label)
 97 | 
 98 |     cls_num = len(prob_Test[0])
 99 |     gt_labels = np.zeros((sample_num, cls_num))
100 |     for k in range(len(query_set)):
101 |         sample = query_set[k]
102 |         label = sample['gt_label']
103 |         one_hot_label = np.array([int(i==label) for i in range(cls_num)])
104 |         gt_labels[k, :] = one_hot_label
105 |     
106 |     ### calculate accuracy and auc for multi-class task
107 |     acc = metrics.accuracy_score(Y_test, pred_labels)
108 | 
109 |     cls_aucs = compute_auc(prob_Test, gt_labels)
110 |     mean_auc = np.mean(cls_aucs)
111 | 
112 |     # print ('acc = ', acc, ', auc = ', mean_auc)
113 |     
114 |     return acc, cls_aucs, mean_auc
115 | 
116 | def do_test_multilabel(query_set, data_dir, model_name, cls_num, clf):
117 |     print('run do_test_multilabel ...')
118 | 
119 |     sample_num = len(query_set)
120 |     print ('sample_num = ', sample_num)
121 | 
122 |     ### load test feats
123 |     X_Test = np.load(os.path.join(data_dir, 'test_feats/', model_name + '.npy'))
124 |     print (X_Test.shape)
125 | 
126 |     gt_labels = np.zeros((sample_num, cls_num))
127 |     sample_idx = 0
128 |     for item in query_set:
129 |         img_label = item['gt_label']
130 |         gt_labels[sample_idx, :] = img_label
131 |         sample_idx += 1
132 | 
133 |     prob_Test = clf.predict_proba(X_Test)
134 |     # print(len(prob_Test))
135 | 
136 |     pred_scores = np.zeros((sample_num, cls_num))
137 |     for i in range(cls_num):
138 |         scores_per_cls = prob_Test[i]
139 |         pred_scores[:, i] = scores_per_cls[:, 1]
140 | 
141 |     ### calculate auc and mAP for multi-label task
142 | 
143 |     cls_aucs = compute_auc(pred_scores, gt_labels)
144 |     mean_auc = np.mean(cls_aucs)
145 |     
146 |     cls_aps = compute_ap(pred_scores, gt_labels)
147 |     mean_AP = np.mean(cls_aps)
148 | 
149 |     # print ('mean_auc = ', mean_auc, ', mean_AP = ', mean_AP)
150 | 
151 |     return cls_aucs, mean_auc, cls_aps, mean_AP
152 | 
153 | 
154 | def do_baseline(support_set, query_set, data_dir, model, model_name, fea_dim, cls_num, dataset_type):
155 |     
156 |     test_result = dict()
157 | 
158 |     if dataset_type in ['Retino', 'ColonPath', 'NeoJaundice']:
159 |         clf = train_cls_model_multiclass(support_set, data_dir, model, fea_dim)
160 |         acc, cls_aucs, mean_auc = do_test_multiclass(query_set, data_dir, model_name, clf)
161 |         test_result['accuracy'] = acc
162 |         test_result['mean_auc'] = mean_auc
163 |         test_result['class_aucs'] = cls_aucs
164 |     else:
165 |         clf = train_cls_model_multilabel(support_set, data_dir, model, fea_dim)
166 |         cls_aucs, mean_auc, cls_aps, mean_AP = do_test_multilabel(query_set, data_dir, model_name, cls_num, clf)
167 |         test_result['mean_auc'] = mean_auc
168 |         test_result['class_aucs'] = cls_aucs
169 |         test_result['mean_AP'] = mean_AP
170 |         test_result['class_aps'] = cls_aps
171 |     
172 |     return test_result
173 | 


--------------------------------------------------------------------------------
/data/NeoJaundice/fewshot-pool.txt:
--------------------------------------------------------------------------------
  1 | 0012-1 0
  2 | 0012-2 0
  3 | 0012-3 0
  4 | 0016-1 0
  5 | 0016-2 0
  6 | 0016-3 0
  7 | 0022-1 1
  8 | 0022-2 1
  9 | 0022-3 1
 10 | 0032-1 1
 11 | 0032-2 1
 12 | 0032-3 1
 13 | 0033-1 0
 14 | 0033-2 0
 15 | 0033-3 0
 16 | 0046-1 1
 17 | 0046-2 1
 18 | 0046-3 1
 19 | 0058-1 1
 20 | 0058-2 1
 21 | 0058-3 1
 22 | 0078-1 0
 23 | 0078-2 0
 24 | 0078-3 0
 25 | 0079-1 0
 26 | 0079-2 0
 27 | 0079-3 0
 28 | 0095-1 0
 29 | 0095-2 0
 30 | 0095-3 0
 31 | 0087-1 1
 32 | 0087-2 1
 33 | 0087-3 1
 34 | 0086-1 1
 35 | 0086-2 1
 36 | 0086-3 1
 37 | 0109-1 0
 38 | 0109-2 0
 39 | 0109-3 0
 40 | 0100-1 1
 41 | 0100-2 1
 42 | 0100-3 1
 43 | 0140-1 0
 44 | 0140-2 0
 45 | 0140-3 0
 46 | 0139-1 0
 47 | 0139-2 0
 48 | 0139-3 0
 49 | 0150-1 0
 50 | 0150-2 0
 51 | 0150-3 0
 52 | 0156-1 0
 53 | 0156-2 0
 54 | 0156-3 0
 55 | 0214-1 0
 56 | 0214-2 0
 57 | 0214-3 0
 58 | 0185-1 0
 59 | 0185-2 0
 60 | 0185-3 0
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--------------------------------------------------------------------------------
/baseline/metabaseline_func.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import numpy as np
  3 | from extract_feats import extract_model_fea
  4 | from sklearn import metrics
  5 | from compute_metric import compute_auc, compute_ap
  6 | import torch
  7 | import math
  8 | 
  9 | def compute_cls_centers(support_set, data_dir, model, fea_dim, is_multiclass=True):
 10 | 
 11 |     print ('run compute_cls_centers ...')
 12 |     
 13 |     cls_num = len(support_set)
 14 |     cls_centers = []
 15 |     for cls_idx in range(cls_num):
 16 |         imgs_per_cls = support_set[cls_idx]
 17 |         cls_center = torch.zeros(fea_dim)
 18 |         for item in imgs_per_cls:
 19 |             image_name = None
 20 |             if is_multiclass:
 21 |                 image_name = item
 22 |             else:
 23 |                 image_name = item[0]
 24 |             img_file = os.path.join(data_dir, 'images/', image_name)
 25 | 
 26 |             ###add png or jpg 20231005
 27 |             img_file = img_file + '.jpg'
 28 |             if not os.path.exists(img_file):
 29 |                 img_file = img_file.replace('.jpg', '.png')
 30 |             
 31 |             img_fea = extract_model_fea(model, img_file)
 32 |             cls_center = cls_center + img_fea
 33 |         cls_center = cls_center / len(imgs_per_cls)
 34 |         cls_center = cls_center / cls_center.norm()
 35 |         
 36 |         cls_centers.append(cls_center)
 37 |     
 38 |     # print (cls_centers)
 39 | 
 40 |     return cls_centers
 41 | 
 42 | def do_meta_test_multiclass(query_set, cls_centers, data_dir, model_name):
 43 |     
 44 |     print ('run do_meta_test_multiclass ...')
 45 | 
 46 |     ### load test feats
 47 |     X_Test = np.load(os.path.join(data_dir, 'test_feats/', model_name + '.npy'))
 48 |     print (X_Test.shape)
 49 | 
 50 |     sample_num = len(query_set)
 51 |     cls_num = len(cls_centers)
 52 | 
 53 |     correct_num = 0
 54 |     cosine_scores_query_set = []
 55 |     count = 0
 56 |     for item in query_set:    
 57 |         img_label = item['gt_label']
 58 |         img_fea = X_Test[count, :]
 59 |         img_fea = img_fea.astype(np.float32)
 60 |         
 61 |         max_cosine = 0
 62 |         pred_cls = -1
 63 |         cosine_scores_per_img = []
 64 |         for cls_idx in range(cls_num):
 65 |             cls_center = cls_centers[cls_idx]
 66 |             # print (type(cls_center), cls_center.shape)
 67 |             img_fea_tensor = torch.from_numpy(img_fea)
 68 |             # print (type(img_fea_tensor), img_fea_tensor.shape)
 69 |             norm_inner = torch.inner(img_fea_tensor, cls_center)
 70 |             cosine_simlarity = norm_inner.item()
 71 |             # print (cls_idx, cosine_simlarity)
 72 |             cosine_scores_per_img.append(cosine_simlarity)
 73 |             if max_cosine < cosine_simlarity:
 74 |                  max_cosine = cosine_simlarity
 75 |                  pred_cls = cls_idx
 76 |         
 77 |         if img_label == pred_cls:
 78 |             correct_num += 1
 79 |             
 80 |         cosine_scores_query_set.append(cosine_scores_per_img)
 81 |         count += 1
 82 | 
 83 |     
 84 |     acc = correct_num / len(query_set)
 85 |             
 86 |     # print ('correct_num = ', correct_num, 'pred_accuracy = ', pred_accuracy)
 87 |     # print (len(cosine_scores_query_set), cosine_scores_query_set[0])
 88 | 
 89 |     ### cpmpute AUC
 90 |     gt_labels = np.zeros((sample_num, cls_num))
 91 |     for k in range(sample_num):
 92 |         sample = query_set[k]
 93 |         label = sample['gt_label']
 94 |         one_hot_label = np.array([int(i==label) for i in range(cls_num)])
 95 |         gt_labels[k, :] = one_hot_label
 96 | 
 97 |     cls_scores = np.zeros((sample_num, cls_num))
 98 |     for k in range(sample_num):
 99 |         cos_score = cosine_scores_query_set[k]
100 |         norm_scores = [math.exp(v) for v in cos_score]
101 |         norm_scores /= np.sum(norm_scores)
102 |         
103 |         cls_scores[k, :] = np.array(norm_scores)
104 |         
105 |     cls_aucs = compute_auc(cls_scores, gt_labels)    
106 |     # print (np.mean(cls_aucs), cls_aucs)
107 |     mean_auc = np.mean(cls_aucs)
108 |     
109 |     return acc, cls_aucs, mean_auc
110 | 
111 | def do_meta_test_multilabel(query_set, cls_centers, data_dir, model_name):
112 |     
113 |     print ('run do_meta_test_multilabel ...')
114 | 
115 |     ### load test feats
116 |     X_Test = np.load(os.path.join(data_dir, 'test_feats/', model_name + '.npy'))
117 |     print (X_Test.shape)
118 | 
119 |     sample_num = len(query_set)
120 |     cls_num = len(cls_centers)
121 | 
122 |     correct_num = 0
123 |     cosine_scores_query_set = []
124 |     count = 0
125 |     for item in query_set:    
126 |         img_label = item['gt_label']
127 |         img_fea = X_Test[count, :]
128 |         img_fea = img_fea.astype(np.float32)
129 |         
130 |         max_cosine = 0
131 |         cosine_scores_per_img = []
132 |         for cls_idx in range(cls_num):
133 |             cls_center = cls_centers[cls_idx]
134 |             # print (type(cls_center), cls_center.shape)
135 |             img_fea_tensor = torch.from_numpy(img_fea)
136 |             # print (type(img_fea_tensor), img_fea_tensor.shape)
137 |             norm_inner = torch.inner(img_fea_tensor, cls_center)
138 |             cosine_simlarity = norm_inner.item()
139 |             # print (cls_idx, cosine_simlarity)
140 |             cosine_scores_per_img.append(cosine_simlarity)
141 |             
142 |         cosine_scores_query_set.append(cosine_scores_per_img)
143 |         count += 1
144 | 
145 |     ### cpmpute AUC
146 |     gt_labels = np.zeros((sample_num, cls_num))
147 |     for k in range(sample_num):
148 |         sample = query_set[k]
149 |         label = sample['gt_label']
150 |         # one_hot_label = np.array([int(i==label) for i in range(cls_num)])
151 |         gt_labels[k, :] = label
152 | 
153 |     cls_scores = np.zeros((sample_num, cls_num))
154 |     for k in range(sample_num):
155 |         cos_score = cosine_scores_query_set[k]
156 |         norm_scores = [math.exp(v) for v in cos_score]
157 |         norm_scores /= np.sum(norm_scores)
158 |         
159 |         cls_scores[k, :] = np.array(norm_scores)
160 |         
161 |     cls_aucs = compute_auc(cls_scores, gt_labels)    
162 |     mean_auc = np.mean(cls_aucs)
163 | 
164 |     cls_aps = compute_ap(cls_scores, gt_labels)
165 |     mean_AP = np.mean(cls_aps)
166 |     
167 |     return cls_aucs, mean_auc, cls_aps, mean_AP
168 | 
169 | def do_metabaseline(support_set, query_set, data_dir, model, model_name, fea_dim, cls_num, dataset_type):
170 |     
171 |     test_result = dict()
172 | 
173 |     if dataset_type in ['Retino', 'ColonPath', 'NeoJaundice']:
174 |         cls_centers = compute_cls_centers(support_set, data_dir, model, fea_dim, is_multiclass=True)
175 |         acc, cls_aucs, mean_auc = do_meta_test_multiclass(query_set, cls_centers, data_dir, model_name)
176 |         # acc, cls_aucs, mean_auc = do_test_multiclass(query_set, data_dir, model_name, clf)
177 |         test_result['accuracy'] = acc
178 |         test_result['mean_auc'] = mean_auc
179 |         test_result['class_aucs'] = cls_aucs
180 |     else:
181 |         cls_centers = compute_cls_centers(support_set, data_dir, model, fea_dim, is_multiclass=False)
182 |         cls_aucs, mean_auc, cls_aps, mean_AP = do_meta_test_multilabel(query_set, cls_centers, data_dir, model_name)
183 |         test_result['mean_auc'] = mean_auc
184 |         test_result['class_aucs'] = cls_aucs
185 |         test_result['mean_AP'] = mean_AP
186 |         test_result['class_aps'] = cls_aps
187 |     
188 |     return test_result
189 | 


--------------------------------------------------------------------------------
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283 | 


--------------------------------------------------------------------------------
/baseline/gen_support_set.py:
--------------------------------------------------------------------------------
  1 | import os
  2 | import random
  3 | from extract_feats import make_dir
  4 | import numpy as np
  5 | 
  6 | ### Retino 
  7 | def gen_support_set_retino(img_list, N_way, K_shot):
  8 |     
  9 |     support_set = []
 10 |     for cls_idx in range(N_way):
 11 |         imgs_per_cls = []
 12 |         for item in img_list:
 13 |             label = item['gt_label']
 14 |             if label == cls_idx:
 15 |                 imgs_per_cls.append(item['filename'])
 16 |                 
 17 |         # print (cls_idx, len(imgs_per_cls), imgs_per_cls[0])
 18 |         
 19 |         sample_set = None
 20 |         if len(imgs_per_cls) <= K_shot:
 21 |             sample_set = imgs_per_cls
 22 |         else:
 23 |             random.shuffle(imgs_per_cls)
 24 |             sample_set = imgs_per_cls[:K_shot]
 25 |         
 26 |         support_set.append(sample_set)            
 27 |         
 28 |     return support_set
 29 | 
 30 | ### NeoJaundice / ColonPath
 31 | def gen_support_set_twoclass(img_list, K_shot, dataset_type):
 32 | 
 33 |     ### sort study_id
 34 |     pos_study_ids = []
 35 |     neg_study_ids = []
 36 |     for item in img_list:
 37 |         img_name = item['filename']
 38 |         study_id = None
 39 |         if dataset_type == 'NeoJaundice':
 40 |             study_id = img_name[:4]
 41 |         elif dataset_type == 'ColonPath':
 42 |             study_id = img_name[:-9]
 43 |         label = item['gt_label']
 44 | 
 45 |         # print (img_name, study_id, label)
 46 | 
 47 |         if label == 1 and study_id not in pos_study_ids:
 48 |             pos_study_ids.append(study_id)
 49 |         if label == 0 and study_id not in neg_study_ids:
 50 |             neg_study_ids.append(study_id)
 51 | 
 52 |     # print (len(pos_study_ids), len(neg_study_ids))
 53 | 
 54 |     random.shuffle(pos_study_ids)
 55 |     random.shuffle(neg_study_ids)
 56 | 
 57 |     pick_pos_study_ids = pos_study_ids[:K_shot]
 58 |     pick_neg_study_ids = neg_study_ids[:K_shot]
 59 | 
 60 |     support_pos_set = []
 61 |     support_neg_set = []
 62 |     for item in img_list:
 63 |         img_name = item['filename']
 64 |         study_id = None
 65 |         if dataset_type == 'NeoJaundice':
 66 |             study_id = img_name[:4]
 67 |         elif dataset_type == 'ColonPath':
 68 |             study_id = img_name[:-9]
 69 | 
 70 |         label = item['gt_label']
 71 | 
 72 |         if study_id in pick_pos_study_ids and label == 1:
 73 |             support_pos_set.append(img_name)
 74 |         if study_id in pick_neg_study_ids and label == 0:
 75 |             support_neg_set.append(img_name)
 76 | 
 77 |     print (len(support_pos_set), support_pos_set[0])
 78 |     print (len(support_neg_set), support_neg_set[0])
 79 | 
 80 |     support_set = []
 81 |     support_set.append(support_neg_set)
 82 |     support_set.append(support_pos_set)
 83 | 
 84 |     return support_set
 85 | 
 86 | ### Endo
 87 | def gen_support_set_endo(img_list, N_way, K_shot):
 88 | 
 89 |     support_set = []
 90 |     for cls_idx in range(N_way):
 91 |         study_ids_per_cls = []
 92 |         for item in img_list:
 93 |             img_name = item['filename']
 94 |             label = item['gt_label']
 95 |             study_id = img_name[:18]
 96 |             if label[cls_idx] == 1 and study_id not in study_ids_per_cls:
 97 |                 study_ids_per_cls.append(study_id)
 98 | 
 99 |         # print (cls_idx, len(study_ids_per_cls))
100 | 
101 |         random.shuffle(study_ids_per_cls)
102 |         support_study_ids_per_cls = study_ids_per_cls[:K_shot]
103 | 
104 |         img_list_per_cls = []
105 |         for item in img_list:
106 |             img_name = item['filename']
107 |             label = item['gt_label']
108 |             study_id = img_name[:18]
109 |             if label[cls_idx] == 1 and study_id in support_study_ids_per_cls:
110 |                 img_list_per_cls.append([img_name, label])      ### modify for baseline 20230303
111 | 
112 |         print (cls_idx, len(img_list_per_cls))
113 |         support_set.append(img_list_per_cls)
114 | 
115 |     return support_set
116 | 
117 | ### ChestDR
118 | def gen_support_set_chest(img_list, N_way, K_shot):
119 |     
120 |     support_set = []
121 |     for cls_idx in range(N_way):
122 |         imgs_per_cls = []
123 |         for item in img_list:
124 |             label = item['gt_label']
125 |             if label[cls_idx] == 1:
126 |                 imgs_per_cls.append([item['filename'], label])
127 |                 
128 |         # print (cls_idx, len(imgs_per_cls), imgs_per_cls[0])
129 |         
130 |         sample_set = None
131 |         if len(imgs_per_cls) <= K_shot:
132 |             sample_set = imgs_per_cls
133 |         else:
134 |             random.shuffle(imgs_per_cls)
135 |             sample_set = imgs_per_cls[:K_shot]
136 |         
137 |         support_set.append(sample_set)            
138 |         
139 |     return support_set
140 | 
141 | def gen_support_set_interface(img_list, N_way, K_shot, dataset_type):
142 | 
143 |     support_set = []
144 | 
145 |     if dataset_type == 'Retino':
146 |         support_set = gen_support_set_retino(img_list, N_way, K_shot)
147 |     elif dataset_type == 'NeoJaundice' or dataset_type == 'ColonPath':
148 |         support_set = gen_support_set_twoclass(img_list, K_shot, dataset_type)
149 |     elif dataset_type == 'Endo':
150 |         support_set = gen_support_set_endo(img_list, N_way, K_shot)
151 |     elif dataset_type == 'ChestDR':
152 |         support_set = gen_support_set_chest(img_list, N_way, K_shot)
153 | 
154 |     return support_set
155 | 
156 | def save_support_list(support_set, dataset_type, save_file):
157 |     fp = open(save_file, 'w')
158 |     cls_num = len(support_set)
159 |     for cls_idx in range(cls_num):
160 |         imgs_per_cls = support_set[cls_idx]
161 |         for item in imgs_per_cls:
162 |             image_name = None
163 |             if dataset_type in ['Retino', 'ColonPath', 'NeoJaundice']:
164 |                 image_name = item
165 |             else:
166 |                 image_name = item[0]
167 |             
168 |             fp.write('image name: {}, class id: {}\n'.format(image_name, cls_idx))
169 | 
170 |     fp.close()
171 | 
172 | def save_fewshot_data(job_dir, dataset_type, support_sets, test_results):
173 |     print ('\nstart save_fewshot_data ...')
174 |     
175 |     ### 1. save support list
176 |     save_list_dir = os.path.join(job_dir, 'support_list/')
177 |     make_dir(save_list_dir)
178 |     count = 0
179 |     for one_set in support_sets:
180 |         # print (item)
181 |         save_list_file = os.path.join(save_list_dir, 'suppot_list_iter_{}.txt'.format(count))
182 |         save_support_list(one_set, dataset_type, save_list_file)
183 |         count += 1
184 | 
185 |     ### 2. save test result
186 |     save_result_file = os.path.join(job_dir, 'test_result.txt')
187 |     fp = open(save_result_file, 'w')
188 | 
189 |     all_acc_set = []
190 |     all_auc_set = []
191 |     all_map_set = []
192 | 
193 |     for one_test in test_results:
194 |         all_auc_set.append(one_test['mean_auc'])
195 |         if dataset_type in ['Retino', 'ColonPath', 'NeoJaundice']:
196 |             all_acc_set.append(one_test['accuracy'])
197 |         else:
198 |             all_map_set.append(one_test['mean_AP'])
199 | 
200 |     if len(all_acc_set) > 0:
201 |         print ('overall accuracy: mean = {:.3f}, std = {:.3f}'.format(np.mean(all_acc_set), np.std(all_acc_set)))
202 |         fp.write ('overall accuracy: mean = {:.3f}, std = {:.3f}\n'.format(np.mean(all_acc_set), np.std(all_acc_set)))
203 |     if len(all_auc_set) > 0:
204 |         print ('overall AUC: mean = {:.3f}, std = {:.3f}'.format(np.mean(all_auc_set), np.std(all_auc_set)))
205 |         fp.write ('overall AUC: mean = {:.3f}, std = {:.3f}\n'.format(np.mean(all_auc_set), np.std(all_auc_set)))
206 |     if len(all_map_set) > 0:
207 |         print ('overall mAP: mean = {:.3f}, std = {:.3f}'.format(np.mean(all_map_set), np.std(all_map_set)))
208 |         fp.write ('overall mAP: mean = {:.3f}, std = {:.3f}\n'.format(np.mean(all_map_set), np.std(all_map_set)))
209 | 
210 |     ### save result of each class
211 |     if dataset_type in ['Retino', 'Endo', 'ChestDR']:
212 |         cls_auc_set = []
213 |         cls_map_set = []
214 | 
215 |         for one_test in test_results:
216 |             cls_auc_set.append(one_test['class_aucs'])
217 |             if dataset_type != 'Retino':
218 |                 cls_map_set.append(one_test['class_aps'])
219 | 
220 |         cls_auc_arr = np.array(cls_auc_set)
221 |         # print (cls_auc_arr.shape, cls_auc_arr)
222 |         for i in range(cls_auc_arr.shape[1]):
223 |             aucs_per_cls = cls_auc_arr[:, i]
224 |             # print (aucs_per_cls)
225 |             print ('class {} auc: mean = {:.3f}, std = {:.3f}'.format(i, np.mean(aucs_per_cls), np.std(aucs_per_cls)))
226 |             fp.write ('class {} auc: mean = {:.3f}, std = {:.3f}\n'.format(i, np.mean(aucs_per_cls), np.std(aucs_per_cls)))
227 | 
228 | 
229 |         if len(cls_map_set) > 0:
230 |             cls_map_arr = np.array(cls_map_set)
231 |             for i in range(cls_map_arr.shape[1]):
232 |                 maps_per_cls = cls_map_arr[:, i]
233 |                 # print (aucs_per_cls)
234 |                 print ('class {} mAP: mean = {:.3f}, std = {:.3f}'.format(i, np.mean(maps_per_cls), np.std(maps_per_cls)))
235 |                 fp.write ('class {} mAP: mean = {:.3f}, std = {:.3f}\n'.format(i, np.mean(maps_per_cls), np.std(maps_per_cls)))
236 | 
237 |         
238 | 
239 | 
240 |     fp.close()
241 | 


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--------------------------------------------------------------------------------
/data/NeoJaundice/test.txt:
--------------------------------------------------------------------------------
   1 | 0003-1 0
   2 | 0003-2 0
   3 | 0003-3 0
   4 | 0035-1 0
   5 | 0035-2 0
   6 | 0035-3 0
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  11 | 0001-2 0
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  23 | 0009-2 1
  24 | 0009-3 1
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--------------------------------------------------------------------------------
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851 | 13333_2021.10_0001_53187993 1 1 0 0
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928 | 13333_2021.04_0000_44779353 0 0 0 0
929 | 13333_2021.04_0000_44779488 0 0 0 0
930 | 


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