├── LICENSE
├── README.md
├── datacsv
├── TCGA.csv
├── camel_egg.csv
└── camelyon16.csv
├── datasets
└── datasets.py
├── main.py
├── models
├── __init__.py
└── models.py
└── utils
├── __init__.py
├── core.py
└── utils.py
/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2023 Hust Vision Lab
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/README.md:
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1 | # Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification
2 |
3 | ## Project Info
4 | This project is the official implementation of MMIL-Transformer proposed in paper [Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classification](https://arxiv.org/abs/2306.05029)
5 |
6 | ## News
7 | A new grouping method(MSA grouping) and the corresponding pre-trained weights will be updated soon.
8 |
9 | ## Prerequisites
10 |
11 | * Python 3.8.10
12 | * Pytorch 1.12.1
13 | * torchmetrics 0.4.1
14 | * CUDA 11.6
15 | * numpy 1.24.2
16 | * einops 0.6.0
17 | * sklearn 1.2.2
18 | * h5py 3.8.0
19 | * pandas 2.0.0
20 | * nystrom_attention
21 | * argparse
22 |
23 | ## Pretrained Weight
24 | All test experiments were conducted 10 times to calculate the average ACC and AUC.
25 |
26 |
27 | | model name | grouping method | weight | ACC | AUC |
28 | |------------|-----|:------:|----|----|
29 | | `TCGA_embed`|Embedding grouping|[HF link](https://huggingface.co/RJKiseki/MMIL-Transformrt/blob/main/TCGA_embed.pt) | 93.15% | 98.97% |
30 | | `TCGA_random`|Random grouping|[HF link](https://huggingface.co/RJKiseki/MMIL-Transformrt/blob/main/TCGA_random.pt) | 94.37%| 99.04% |
31 | | `TCGA_random_with_subbags_0.75masked`|Random grouping + mask|[HF link](https://huggingface.co/RJKiseki/MMIL-Transformrt/blob/main/TCGA_random_mask_0.75.pt) | 93.95%| 99.02% |
32 | | `camelyon16_random`|Random grouping|[HF link](https://huggingface.co/RJKiseki/MMIL-Transformrt/blob/main/camelyon16_random.pt) | 91.78% | 94.07% |
33 | | `camelyon16_random_with_subbags_0.6masked`| Random grouping + mask|[HF link](https://huggingface.co/RJKiseki/MMIL-Transformrt/blob/main/camelyon16_mask_0.6.pt) | 93.41% | 94.74% |
34 |
35 |
36 |
37 | ## Usage
38 | ### Dataset
39 |
40 | #### Preprocess TCGA Dataset
41 |
42 | >We use the same configuration of data preprocessing as [DSMIL](https://github.com/binli123/dsmil-wsi). Or you can directly download the feature vector they provided for TCGA.
43 |
44 | #### Preprocess CAMELYON16 Dataset
45 |
46 | >We use [CLAM](https://github.com/mahmoodlab/CLAM/tree/master) to preprocess CAMELYON16 at 20x.
47 |
48 | #### Preprocessed feature vector
49 |
50 | >Preprocess WSI is time consuming and difficult. We also provide processed feature vector for two datasets. Aforementioned works [DSMIL](https://github.com/binli123/dsmil-wsi) and [CLAM](https://github.com/mahmoodlab/CLAM/tree/master)
51 | greatly simplified the preprocessing. Thanks again to their wonderful works!
52 |
53 |
54 |
55 | | Dataset | Link | Disk usage |
56 | |------------|:-----:|----|
57 | | `TCGA`|[HF link](https://huggingface.co/datasets/RJKiseki/TCGA/tree/main)| 16GB |
58 | | `CAMELYON16`|[HF link](https://huggingface.co/datasets/RJKiseki/CAMELYON16/tree/main)|20GB|
59 |
60 |
61 |
62 | ### Test the model
63 |
64 | For TCGA testing:
65 | ```
66 | python main.py \
67 | --test {Your_Path_to_Pretrain} \
68 | --num_test 10 \
69 | --type TCGA \
70 | --num_subbags 4 \
71 | --mode {embed or random} \
72 | --num_msg 1 \
73 | --num_layers 2 \
74 | --csv {Your_Path_to_TCGA_csv} \
75 | --h5 {Your_Path_to_h5_file}
76 | ```
77 |
78 |
79 | For CAMELYON16 testing:
80 | ```
81 | python main.py \
82 | --test {Your_Path_to_Pretrain} \
83 | --num_test 10 \
84 | --type camelyon16 \
85 | --num_subbags 10 \
86 | --mode random \
87 | --num_msg 1 \
88 | --num_layers 2 \
89 | --csv {Your_Path_to_CAMELYON16_csv}\
90 | --h5 {Your_Path_to_h5_file}
91 | ```
92 |
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/datacsv/TCGA.csv:
--------------------------------------------------------------------------------
1 | train,train_label,val,val_label,test,test_label
2 | data_tcga_lung_tree/TCGA-22-4595-01Z-00-DX1,1,data_tcga_lung_tree/TCGA-69-A59K-01Z-00-DX1,0.0,data_tcga_lung_tree/TCGA-97-7553-01Z-00-DX1,0.0
3 | data_tcga_lung_tree/TCGA-56-A4BW-01Z-00-DX1,1,data_tcga_lung_tree/TCGA-80-5608-01Z-00-DX1,0.0,data_tcga_lung_tree/TCGA-56-6546-01Z-00-DX1,1.0
4 | data_tcga_lung_tree/TCGA-33-4547-01Z-00-DX5,1,data_tcga_lung_tree/TCGA-63-7023-01Z-00-DX1,1.0,data_tcga_lung_tree/TCGA-97-A4M5-01Z-00-DX1,0.0
5 | data_tcga_lung_tree/TCGA-49-6745-01Z-00-DX4,0,data_tcga_lung_tree/TCGA-63-5128-01Z-00-DX1,1.0,data_tcga_lung_tree/TCGA-49-6742-01Z-00-DX5,0.0
6 | data_tcga_lung_tree/TCGA-NK-A5CT-01Z-00-DX1,1,data_tcga_lung_tree/TCGA-49-4507-01Z-00-DX2,0.0,data_tcga_lung_tree/TCGA-50-6593-01Z-00-DX1,0.0
7 | data_tcga_lung_tree/TCGA-43-A475-01Z-00-DX1,1,data_tcga_lung_tree/TCGA-86-8076-01Z-00-DX1,0.0,data_tcga_lung_tree/TCGA-55-8302-01Z-00-DX1,0.0
8 | data_tcga_lung_tree/TCGA-33-4538-01Z-00-DX4,1,data_tcga_lung_tree/TCGA-NK-A5CX-01Z-00-DX1,1.0,data_tcga_lung_tree/TCGA-49-AAR0-01Z-00-DX1,0.0
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26 | data_tcga_lung_tree/TCGA-85-A4QR-01Z-00-DX1,1,data_tcga_lung_tree/TCGA-39-5027-01Z-00-DX1,1.0,data_tcga_lung_tree/TCGA-50-6591-01Z-00-DX1,0.0
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575 | data_tcga_lung_tree/TCGA-22-4591-01Z-00-DX1,1,,,,
576 | data_tcga_lung_tree/TCGA-05-4390-01Z-00-DX1,0,,,,
577 | data_tcga_lung_tree/TCGA-MP-A4SY-01Z-00-DX1,0,,,,
578 | data_tcga_lung_tree/TCGA-43-7656-01Z-00-DX1,1,,,,
579 | data_tcga_lung_tree/TCGA-77-7141-01Z-00-DX1,1,,,,
580 | data_tcga_lung_tree/TCGA-85-6561-01Z-00-DX1,1,,,,
581 | data_tcga_lung_tree/TCGA-94-A4VJ-01Z-00-DX1,1,,,,
582 | data_tcga_lung_tree/TCGA-MN-A4N4-01Z-00-DX2,0,,,,
583 | data_tcga_lung_tree/TCGA-97-7937-01Z-00-DX1,0,,,,
584 | data_tcga_lung_tree/TCGA-21-1081-01Z-00-DX1,1,,,,
585 | data_tcga_lung_tree/TCGA-85-8049-01Z-00-DX1,1,,,,
586 | data_tcga_lung_tree/TCGA-99-8025-01Z-00-DX1,0,,,,
587 | data_tcga_lung_tree/TCGA-49-AARQ-01Z-00-DX1,0,,,,
588 | data_tcga_lung_tree/TCGA-86-8279-01Z-00-DX1,0,,,,
589 | data_tcga_lung_tree/TCGA-49-4490-01Z-00-DX3,0,,,,
590 | data_tcga_lung_tree/TCGA-55-7281-01Z-00-DX1,0,,,,
591 | data_tcga_lung_tree/TCGA-93-7347-01Z-00-DX1,0,,,,
592 | data_tcga_lung_tree/TCGA-MP-A4SV-01Z-00-DX1,0,,,,
593 | data_tcga_lung_tree/TCGA-53-7626-01Z-00-DX1,0,,,,
594 | data_tcga_lung_tree/TCGA-43-2578-01Z-00-DX1,1,,,,
595 | data_tcga_lung_tree/TCGA-77-8007-01Z-00-DX1,1,,,,
596 | data_tcga_lung_tree/TCGA-67-3773-01Z-00-DX1,0,,,,
597 | data_tcga_lung_tree/TCGA-44-6145-01Z-00-DX1,0,,,,
598 | data_tcga_lung_tree/TCGA-50-5068-01Z-00-DX1,0,,,,
599 | data_tcga_lung_tree/TCGA-22-0940-01Z-00-DX1,1,,,,
600 | data_tcga_lung_tree/TCGA-78-7154-01Z-00-DX1,0,,,,
601 | data_tcga_lung_tree/TCGA-05-4432-01Z-00-DX1,0,,,,
602 | data_tcga_lung_tree/TCGA-90-A59Q-01Z-00-DX1,1,,,,
603 | data_tcga_lung_tree/TCGA-97-A4M2-01Z-00-DX1,0,,,,
604 | data_tcga_lung_tree/TCGA-NC-A5HQ-01Z-00-DX1,1,,,,
605 | data_tcga_lung_tree/TCGA-55-7903-01Z-00-DX1,0,,,,
606 | data_tcga_lung_tree/TCGA-99-7458-01Z-00-DX1,0,,,,
607 | data_tcga_lung_tree/TCGA-86-8280-01Z-00-DX1,0,,,,
608 | data_tcga_lung_tree/TCGA-78-7537-01Z-00-DX1,0,,,,
609 | data_tcga_lung_tree/TCGA-38-7271-01Z-00-DX1,0,,,,
610 | data_tcga_lung_tree/TCGA-55-A493-01Z-00-DX1,0,,,,
611 | data_tcga_lung_tree/TCGA-49-4488-01Z-00-DX5,0,,,,
612 | data_tcga_lung_tree/TCGA-NK-A7XE-01Z-00-DX1,1,,,,
613 | data_tcga_lung_tree/TCGA-05-4417-01Z-00-DX1,0,,,,
614 | data_tcga_lung_tree/TCGA-43-A56U-01Z-00-DX1,1,,,,
615 | data_tcga_lung_tree/TCGA-46-3766-01Z-00-DX1,1,,,,
616 | data_tcga_lung_tree/TCGA-63-A5MI-01Z-00-DX1,1,,,,
617 | data_tcga_lung_tree/TCGA-56-8623-01Z-00-DX1,1,,,,
618 | data_tcga_lung_tree/TCGA-55-A4DG-01Z-00-DX1,0,,,,
619 | data_tcga_lung_tree/TCGA-44-2656-01Z-00-DX1,0,,,,
620 | data_tcga_lung_tree/TCGA-44-3919-01Z-00-DX1,0,,,,
621 | data_tcga_lung_tree/TCGA-86-8056-01Z-00-DX1,0,,,,
622 | data_tcga_lung_tree/TCGA-21-1082-01Z-00-DX1,1,,,,
623 | data_tcga_lung_tree/TCGA-70-6722-01Z-00-DX1,1,,,,
624 | data_tcga_lung_tree/TCGA-77-8131-01Z-00-DX1,1,,,,
625 | data_tcga_lung_tree/TCGA-55-1596-01Z-00-DX1,0,,,,
626 | data_tcga_lung_tree/TCGA-39-5028-01Z-00-DX1,1,,,,
627 | data_tcga_lung_tree/TCGA-44-2666-01Z-00-DX1,0,,,,
628 | data_tcga_lung_tree/TCGA-44-2664-01Z-00-DX1,0,,,,
629 |
--------------------------------------------------------------------------------
/datacsv/camel_egg.csv:
--------------------------------------------------------------------------------
1 | ,train,train_label,test,test_label
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--------------------------------------------------------------------------------
/datacsv/camelyon16.csv:
--------------------------------------------------------------------------------
1 | train,train_label,val,val_label,test,test_label
2 | normal_083,0.0,normal_067,0.0,test_001,1.0
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7 | normal_047,0.0,tumor_101,1.0,test_006,0.0
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9 | normal_075,0.0,normal_069,0.0,test_008,1.0
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11 | normal_005,0.0,normal_122,0.0,test_010,1.0
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13 | normal_044,0.0,normal_126,0.0,test_012,0.0
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15 | normal_020,0.0,normal_156,0.0,test_014,0.0
16 | normal_064,0.0,normal_095,0.0,test_015,0.0
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110 | normal_006,0.0,,,test_110,1.0
111 | normal_147,0.0,,,test_111,0.0
112 | normal_007,0.0,,,test_112,0.0
113 | tumor_110,1.0,,,test_113,1.0
114 | tumor_085,1.0,,,test_114,1.0
115 | tumor_103,1.0,,,test_115,0.0
116 | tumor_007,1.0,,,test_116,1.0
117 | tumor_030,1.0,,,test_117,1.0
118 | tumor_076,1.0,,,test_118,0.0
119 | tumor_079,1.0,,,test_119,0.0
120 | tumor_041,1.0,,,test_120,0.0
121 | tumor_058,1.0,,,test_121,1.0
122 | tumor_065,1.0,,,test_122,1.0
123 | tumor_017,1.0,,,test_123,0.0
124 | tumor_096,1.0,,,test_124,0.0
125 | tumor_097,1.0,,,test_125,0.0
126 | tumor_034,1.0,,,test_126,0.0
127 | tumor_002,1.0,,,test_127,0.0
128 | tumor_109,1.0,,,test_128,0.0
129 | tumor_064,1.0,,,test_129,0.0
130 | tumor_051,1.0,,,test_130,0.0
131 | tumor_026,1.0,,,,
132 | tumor_045,1.0,,,,
133 | tumor_042,1.0,,,,
134 | tumor_059,1.0,,,,
135 | tumor_087,1.0,,,,
136 | tumor_082,1.0,,,,
137 | tumor_013,1.0,,,,
138 | tumor_092,1.0,,,,
139 | tumor_032,1.0,,,,
140 | tumor_038,1.0,,,,
141 | tumor_111,1.0,,,,
142 | tumor_006,1.0,,,,
143 | tumor_068,1.0,,,,
144 | tumor_091,1.0,,,,
145 | tumor_090,1.0,,,,
146 | tumor_052,1.0,,,,
147 | tumor_100,1.0,,,,
148 | tumor_095,1.0,,,,
149 | tumor_011,1.0,,,,
150 | tumor_098,1.0,,,,
151 | tumor_108,1.0,,,,
152 | tumor_018,1.0,,,,
153 | tumor_074,1.0,,,,
154 | tumor_104,1.0,,,,
155 | tumor_010,1.0,,,,
156 | tumor_062,1.0,,,,
157 | tumor_077,1.0,,,,
158 | tumor_106,1.0,,,,
159 | tumor_061,1.0,,,,
160 | tumor_078,1.0,,,,
161 | tumor_093,1.0,,,,
162 | tumor_027,1.0,,,,
163 | tumor_012,1.0,,,,
164 | tumor_024,1.0,,,,
165 | tumor_020,1.0,,,,
166 | tumor_036,1.0,,,,
167 | tumor_008,1.0,,,,
168 | tumor_004,1.0,,,,
169 | tumor_022,1.0,,,,
170 | tumor_071,1.0,,,,
171 | tumor_019,1.0,,,,
172 | tumor_001,1.0,,,,
173 | tumor_028,1.0,,,,
174 | tumor_048,1.0,,,,
175 | tumor_075,1.0,,,,
176 | tumor_049,1.0,,,,
177 | tumor_005,1.0,,,,
178 | tumor_009,1.0,,,,
179 | tumor_023,1.0,,,,
180 | tumor_031,1.0,,,,
181 | tumor_055,1.0,,,,
182 | tumor_033,1.0,,,,
183 | tumor_094,1.0,,,,
184 | tumor_069,1.0,,,,
185 | tumor_084,1.0,,,,
186 | tumor_040,1.0,,,,
187 | tumor_086,1.0,,,,
188 | tumor_029,1.0,,,,
189 | tumor_044,1.0,,,,
190 | tumor_072,1.0,,,,
191 | tumor_056,1.0,,,,
192 | tumor_070,1.0,,,,
193 | tumor_037,1.0,,,,
194 | tumor_054,1.0,,,,
195 | tumor_021,1.0,,,,
196 | normal_144,0.0,,,,
197 | normal_002,0.0,,,,
198 | normal_014,0.0,,,,
199 | normal_035,0.0,,,,
200 | normal_085,0.0,,,,
201 | normal_155,0.0,,,,
202 | normal_012,0.0,,,,
203 | normal_135,0.0,,,,
204 | normal_142,0.0,,,,
205 | normal_090,0.0,,,,
206 | normal_023,0.0,,,,
207 | normal_152,0.0,,,,
208 | normal_148,0.0,,,,
209 | normal_059,0.0,,,,
210 | normal_078,0.0,,,,
211 | normal_027,0.0,,,,
212 | normal_109,0.0,,,,
213 | normal_157,0.0,,,,
214 | normal_018,0.0,,,,
215 | normal_056,0.0,,,,
216 | normal_088,0.0,,,,
217 | normal_045,0.0,,,,
218 | normal_121,0.0,,,,
219 | normal_125,0.0,,,,
220 | normal_077,0.0,,,,
221 | normal_039,0.0,,,,
222 | normal_103,0.0,,,,
223 | normal_066,0.0,,,,
224 | normal_011,0.0,,,,
225 | tumor_080,1.0,,,,
226 | tumor_107,1.0,,,,
227 | tumor_063,1.0,,,,
228 | tumor_047,1.0,,,,
229 | tumor_016,1.0,,,,
230 | tumor_046,1.0,,,,
231 | tumor_073,1.0,,,,
232 | tumor_003,1.0,,,,
233 | tumor_088,1.0,,,,
234 | tumor_060,1.0,,,,
235 | tumor_099,1.0,,,,
236 | tumor_089,1.0,,,,
237 | tumor_043,1.0,,,,
238 | tumor_015,1.0,,,,
239 | tumor_014,1.0,,,,
240 | tumor_083,1.0,,,,
241 | tumor_035,1.0,,,,
242 | tumor_067,1.0,,,,
243 | tumor_057,1.0,,,,
244 | tumor_081,1.0,,,,
245 |
--------------------------------------------------------------------------------
/datasets/datasets.py:
--------------------------------------------------------------------------------
1 | from torch.utils.data import Dataset
2 | import pandas as pd
3 | import h5py, os
4 | import numpy as np
5 | import torch
6 |
7 | class h5file_Dataset(Dataset):
8 | def __init__(self, csv_file, h5file_dir, datatype):
9 | self.csv_file = pd.read_csv(csv_file)
10 | self.h5file_dir = h5file_dir
11 | self.datatype = datatype
12 | if self.datatype == 'train':
13 | self.csv_index = [self.csv_file.columns.get_loc('train'),self.csv_file.columns.get_loc('train_label')]
14 | self.lenth = self.csv_file['train'].count()
15 | elif self.datatype == 'test':
16 | self.csv_index = [self.csv_file.columns.get_loc('test'),self.csv_file.columns.get_loc('test_label')]
17 | self.lenth = self.csv_file['test'].count()
18 | elif self.datatype == 'val':
19 | self.csv_index = [self.csv_file.columns.get_loc('val'),self.csv_file.columns.get_loc('val_label')]
20 | self.lenth = self.csv_file['val'].count()
21 | def __len__(self):
22 | return self.lenth
23 |
24 | def __getitem__(self, index):
25 | data_dir = os.path.join(self.h5file_dir, self.csv_file.iloc[index, self.csv_index[0]])
26 | data = h5py.File(data_dir+'.h5')
27 | features = np.array(data['features'])
28 | coords = np.array(data['coords'])
29 | label = self.csv_file.iloc[index, self.csv_index[1]]
30 | return coords, features, label
31 |
32 |
33 | class h5file_Dataset_with_Cluster_index(Dataset):
34 | def __init__(self, csv_file, h5file_dir, clusteridx_dir,datatype):
35 | self.csv_file = pd.read_csv(csv_file)
36 | self.h5file_dir = h5file_dir
37 | self.datatype = datatype
38 | self.clusteridx_dir = clusteridx_dir
39 | if self.datatype == 'train':
40 | self.csv_index = [self.csv_file.columns.get_loc('train'),self.csv_file.columns.get_loc('train_label')]
41 | self.lenth = self.csv_file['train'].count()
42 | elif self.datatype == 'test':
43 | self.csv_index = [self.csv_file.columns.get_loc('test'),self.csv_file.columns.get_loc('test_label')]
44 | self.lenth = self.csv_file['test'].count()
45 | elif self.datatype == 'val':
46 | self.csv_index = [self.csv_file.columns.get_loc('val'),self.csv_file.columns.get_loc('val_label')]
47 | self.lenth = self.csv_file['val'].count()
48 |
49 | def __len__(self):
50 | return self.lenth
51 |
52 | def __getitem__(self, index):
53 | data_dir = os.path.join(self.h5file_dir, self.csv_file.iloc[index, self.csv_index[0]])
54 | data_group_idx_dir = os.path.join(self.clusteridx_dir, self.csv_file.iloc[index, self.csv_index[0]])
55 | data = h5py.File(data_dir+'.h5')
56 | data_group_idx = np.load(data_group_idx_dir+'.npy')
57 | features = np.array(data['features'])
58 | label = self.csv_file.iloc[index, self.csv_index[1]]
59 | return data_group_idx,features,label
60 |
61 |
--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
1 | from utils.utils import make_parse
2 | from utils.core import test
3 | from torch.utils.data import DataLoader
4 | from datasets.datasets import h5file_Dataset
5 | from models.models import MultipleMILTransformer as MMILT
6 | import torch
7 | import numpy as np
8 |
9 | def main(args):
10 | torch.manual_seed(2023)
11 | model = MMILT(args).cuda()
12 | data_csv_dir = args.csv
13 | h5file_dir = args.h5
14 |
15 | test_dataset = h5file_Dataset(data_csv_dir,h5file_dir,'test')
16 | test_dataloader = DataLoader(test_dataset, batch_size=1, shuffle=True)
17 |
18 | loader = test_dataloader
19 |
20 | acc = []
21 | auc = []
22 | for i in range(args.num_test):
23 | model.load_state_dict(torch.load(args.test))
24 | test_acc,test_auc = test(args,model,loader)
25 | acc.append(test_acc)
26 | auc.append(test_auc.cpu())
27 | print('Average acc and auc for {} times test is {} and {}'.format(args.num_test,np.mean(acc),np.mean(auc)))
28 |
29 |
30 | args = make_parse()
31 | main(args)
--------------------------------------------------------------------------------
/models/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/hustvl/MMIL-Transformer/d363f43bf0949e3fc172e8d94f99deb335bbe171/models/__init__.py
--------------------------------------------------------------------------------
/models/models.py:
--------------------------------------------------------------------------------
1 | from utils.core import *
2 | from utils.utils import *
3 | import torch
4 | import random
5 | import torch.nn as nn
6 | from einops import rearrange
7 | from nystrom_attention import NystromAttention
8 | import torch.nn.functional as F
9 |
10 | class Attention(nn.Module):
11 | def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
12 | super().__init__()
13 | inner_dim = dim_head * heads
14 | project_out = not (heads == 1 and dim_head == dim)
15 |
16 | self.heads = heads
17 | self.scale = dim_head ** -0.5
18 |
19 | self.attend = nn.Softmax(dim = -1)
20 | self.dropout = nn.Dropout(dropout)
21 |
22 | self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
23 |
24 | self.to_out = nn.Sequential(
25 | nn.Linear(inner_dim, dim),
26 | nn.Dropout(dropout)
27 | ) if project_out else nn.Identity()
28 |
29 | def forward(self, x):
30 | #x = x.squeeze(dim=0)
31 | qkv = self.to_qkv(x).chunk(3, dim = -1)
32 | q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
33 |
34 | dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
35 |
36 | attn = self.attend(dots)
37 | attn = self.dropout(attn)
38 |
39 | out = torch.matmul(attn, v)
40 | out = rearrange(out, 'b h n d -> b n (h d)')
41 | return self.to_out(out)
42 |
43 |
44 | class AttenLayer(nn.Module):
45 | def __init__(self,dim,heads=8,dim_head=64,dropout=0.1,attn_mode='normal'):
46 | super(AttenLayer, self).__init__()
47 | self.dim = dim
48 | self.heads = heads
49 | self.dim_head = dim_head
50 | self.dropout = dropout
51 | self.mode = attn_mode
52 | self.attn = Attention(self.dim,heads=self.heads,dim_head=self.dim_head,dropout=self.dropout)
53 | def forward(self,x):
54 | return x + self.attn(x)
55 |
56 | class NyAttenLayer(nn.Module):
57 | def __init__(self,dim,heads=8,dim_head=64,dropout=0.1):
58 | super(NyAttenLayer, self).__init__()
59 | self.dim = dim
60 | self.heads = heads
61 | self.dim_head = dim_head
62 | self.dropout = dropout
63 | self.attn = NystromAttention(
64 | dim = dim,
65 | dim_head = dim//8,
66 | heads = 8,
67 | num_landmarks = dim//2, # number of landmarks
68 | pinv_iterations = 6, # number of moore-penrose iterations for approximating pinverse. 6 was recommended by the paper
69 | residual = True, # whether to do an extra residual with the value or not. supposedly faster convergence if turned on
70 | dropout=0.1
71 | )
72 | def forward(self,x):
73 | return x + self.attn(x)
74 |
75 | class GroupsAttenLayer(nn.Module):
76 | def __init__(self,dim,heads=8,dim_head=64,dropout=0.1,attn_mode='normal'):
77 | super(GroupsAttenLayer, self).__init__()
78 | self.dim = dim
79 | self.heads = heads
80 | self.dim_head = dim_head
81 | self.dropout = dropout
82 | if attn_mode == 'nystrom':
83 | self.AttenLayer = NyAttenLayer(dim =self.dim,heads=self.heads,dim_head=self.dim_head,dropout=self.dropout)
84 | else:
85 | self.AttenLayer = AttenLayer(dim =self.dim,heads=self.heads,dim_head=self.dim_head,dropout=self.dropout)
86 |
87 | def forward(self,x_groups,mask_ratio=0):
88 | group_after_attn = []
89 | r = int(len(x_groups) * (1-mask_ratio))
90 | x_groups_masked = random.sample(x_groups, k=r)
91 | for x in x_groups_masked:
92 | x = x.squeeze(dim=0)
93 | temp = self.AttenLayer(x).unsqueeze(dim=0)
94 | group_after_attn.append(temp)
95 | return group_after_attn
96 |
97 | class GroupsMSGAttenLayer(nn.Module):
98 | def __init__(self,dim,heads=8,dim_head=64,dropout=0.1):
99 | super().__init__()
100 | self.dim = dim
101 | self.heads = heads
102 | self.dim_head = dim_head
103 | self.dropout = dropout
104 | self.AttenLayer = AttenLayer(dim =self.dim,heads=self.heads,dim_head=self.dim_head,dropout=self.dropout)
105 | def forward(self,data):
106 | msg_cls, x_groups, msg_tokens_num = data
107 | groups_num = len(x_groups)
108 | msges = torch.zeros(size=(1,1,groups_num*msg_tokens_num,self.dim)).to(msg_cls.device)
109 | for i in range(groups_num):
110 | msges[:,:,i*msg_tokens_num:(i+1)*msg_tokens_num,:] = x_groups[i][:,:,0:msg_tokens_num]
111 | msges = torch.cat((msg_cls,msges),dim=2).squeeze(dim=0)
112 | msges = self.AttenLayer(msges).unsqueeze(dim=0)
113 | msg_cls = msges[:,:,0].unsqueeze(dim=0)
114 | msges = msges[:,:,1:]
115 | for i in range(groups_num):
116 | x_groups[i] = torch.cat((msges[:,:,i*msg_tokens_num:(i+1)*msg_tokens_num],x_groups[i][:,:,msg_tokens_num:]),dim=2)
117 | data = msg_cls, x_groups, msg_tokens_num
118 | return data
119 |
120 | class BasicLayer(nn.Module):
121 | def __init__(self,dim):
122 | super().__init__()
123 | self.GroupsAttenLayer = GroupsAttenLayer(dim=dim)
124 | self.GroupsMSGAttenLayer = GroupsMSGAttenLayer(dim=dim)
125 | def forward(self,data,mask_ratio):
126 | msg_cls, x_groups, msg_tokens_num = data
127 | x_groups = self.GroupsAttenLayer(x_groups,mask_ratio)
128 | data = (msg_cls, x_groups, msg_tokens_num)
129 | data = self.GroupsMSGAttenLayer(data)
130 | return data
131 |
132 |
133 | class MultipleMILTransformer(nn.Module):
134 | def __init__(self,args):
135 | super(MultipleMILTransformer, self).__init__()
136 | self.args = args
137 | self.fc1 = nn.Linear(self.args.in_chans, self.args.embed_dim)
138 | self.fc2 = nn.Linear(self.args.embed_dim, self.args.n_classes)
139 | self.msg_tokens_num = self.args.num_msg
140 | self.msgcls_token = nn.Parameter(torch.randn(1,1,1,self.args.embed_dim))
141 | #---> make sub-bags
142 | print('try to group seq to ',self.args.num_subbags)
143 | self.grouping = grouping(self.args.num_subbags,max_size=4300)
144 | if self.args.mode == 'random':
145 | self.grouping_features = self.grouping.random_grouping
146 | elif self.args.mode == 'coords':
147 | self.grouping_features = self.grouping.coords_grouping
148 | elif self.args.mode == 'seq':
149 | self.grouping_features = self.grouping.seqential_grouping
150 | elif self.args.mode == 'embed':
151 | self.grouping_features = self.grouping.embedding_grouping
152 | elif self.args.mode == 'idx':
153 | self.grouping_features = self.grouping.idx_grouping
154 | self.msg_tokens = nn.Parameter(torch.zeros(1, 1, 1, self.args.embed_dim))
155 | self.cat_msg2cluster_group = cat_msg2cluster_group
156 | if self.args.ape:
157 | self.absolute_pos_embed = nn.Parameter(torch.zeros(1, 1, self.args.embed_dim))
158 |
159 | #--->build layers
160 | self.layers = nn.ModuleList()
161 | for i_layer in range(self.args.num_layers):
162 | layer = BasicLayer(dim=self.args.embed_dim)
163 | self.layers.append(layer)
164 |
165 | def head(self,x):
166 | logits = self.fc2(x)
167 | Y_hat = torch.argmax(logits, dim=1)
168 | Y_prob = F.softmax(logits, dim=1)
169 | results_dict = {'logits': logits, 'Y_prob': Y_prob, 'Y_hat': Y_hat}
170 | return results_dict
171 |
172 |
173 | def forward(self,x,coords=False,mask_ratio=0):
174 | #---> init
175 | if self.args.type == 'camelyon16':
176 | x = self.fc1(x)
177 | else:
178 | x = x.float()
179 | if self.args.ape:
180 | x = x + self.absolute_pos_embed.expand(1,x.shape[1],self.args.embed_dim)
181 | if self.args.mode == 'coords' or self.args.mode == 'idx':
182 | x_groups = self.grouping_features(coords,x)
183 | else:
184 | x_groups = self.grouping_features(x)
185 | msg_tokens = self.msg_tokens.expand(1,1,self.msg_tokens_num,-1)
186 | msg_cls = self.msgcls_token
187 | x_groups = self.cat_msg2cluster_group(x_groups,msg_tokens)
188 | data = (msg_cls, x_groups, self.msg_tokens_num)
189 | #---> feature forward
190 | for i in range(len(self.layers)):
191 | if i == 0:
192 | mr = mask_ratio
193 | data = self.layers[i](data,mr)
194 | else:
195 | mr = 0
196 | data = self.layers[i](data,mr)
197 | #---> head
198 | msg_cls, _, _ = data
199 | msg_cls = msg_cls.view(1,self.args.embed_dim)
200 | results_dict = self.head(msg_cls)
201 | #print(results_dict)
202 |
203 | return results_dict
204 |
--------------------------------------------------------------------------------
/utils/__init__.py:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/hustvl/MMIL-Transformer/d363f43bf0949e3fc172e8d94f99deb335bbe171/utils/__init__.py
--------------------------------------------------------------------------------
/utils/core.py:
--------------------------------------------------------------------------------
1 | import torchmetrics
2 | import torch.nn as nn
3 | import torch
4 | from .utils import *
5 | import numpy as np
6 | from sklearn.cluster import KMeans
7 |
8 | torch.manual_seed(2023)
9 |
10 | def test(args,model,dataloader):
11 | np.random.seed(args.seed)
12 | print('-------testing-------')
13 | device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
14 | test_loader = dataloader
15 | loss_fn = nn.CrossEntropyLoss()
16 | model.eval()
17 |
18 | test_loss = 0.
19 | test_error = 0.
20 | with torch.no_grad():
21 | for idx, (coords ,data, label) in enumerate(test_loader):
22 | coords ,data, label =coords.to(device), data.to(device), label.to(device).long()
23 | results_dict = model(data,coords,mask_ratio=0)
24 | logits,Y_prob,Y_hat = results_dict['logits'],results_dict['Y_prob'],results_dict['Y_hat']
25 | if idx == 0:
26 | Y_prob_list = Y_prob
27 | label_list = label
28 | else:
29 | Y_prob_list = torch.cat((Y_prob_list,Y_prob), dim=0)
30 | label_list = torch.cat((label_list,label), dim=0)
31 | loss = loss_fn(logits,label)
32 | test_loss += loss
33 | error = calculate_error(Y_hat,label)
34 | test_error += error
35 | test_auroc = torchmetrics.AUROC(num_classes=2)
36 | test_auc = test_auroc(Y_prob_list, label_list)
37 |
38 | t_hit_num = len(test_loader) - test_error
39 | test_error /= len(test_loader)
40 | test_loss /= len(test_loader)
41 |
42 | print('test_loss: {:.4f}, test_error: {:.4f}'.format(test_loss, test_error))
43 | print('test_correct:',int(t_hit_num),'/',len(test_loader))
44 | print('test_auc: {}'.format(test_auc))
45 | print('-----------------------')
46 |
47 | return 1-test_error, test_auc
48 |
49 | class grouping:
50 |
51 | def __init__(self,groups_num,max_size=1e10):
52 | self.groups_num = groups_num
53 | self.max_size = int(max_size) # Max lenth 4300 for 24G RTX3090
54 |
55 |
56 | def indicer(self, labels):
57 | indices = []
58 | groups_num = len(set(labels))
59 | for i in range(groups_num):
60 | temp = np.argwhere(labels==i).squeeze()
61 | indices.append(temp)
62 | return indices
63 |
64 | def make_subbags(self, idx, features):
65 | index = idx
66 | features_group = []
67 | for i in range(len(index)):
68 | member_size = (index[i].size)
69 | if member_size > self.max_size:
70 | index[i] = np.random.choice(index[i],size=self.max_size,replace=False)
71 | temp = features[index[i]]
72 | temp = temp.unsqueeze(dim=0)
73 | features_group.append(temp)
74 |
75 | return features_group
76 |
77 | def coords_nomlize(self, coords):
78 | coords = coords.squeeze()
79 | means = torch.mean(coords,0)
80 | xmean,ymean = means[0],means[1]
81 | stds = torch.std(coords,0)
82 | xstd,ystd = stds[0],stds[1]
83 | xcoords = (coords[:,0] - xmean)/xstd
84 | ycoords = (coords[:,1] - ymean)/ystd
85 | xcoords,ycoords = xcoords.view(xcoords.shape[0],1),ycoords.view(ycoords.shape[0],1)
86 | coords = torch.cat((xcoords,ycoords),dim=1)
87 |
88 | return coords
89 |
90 |
91 | def coords_grouping(self,coords,features,c_norm=False):
92 | features = features.squeeze()
93 | coords = coords.squeeze()
94 | if c_norm:
95 | coords = self.coords_nomlize(coords.float())
96 | features = features.squeeze()
97 | k = KMeans(n_clusters=self.groups_num, random_state=0,n_init='auto').fit(coords.cpu().numpy())
98 | indices = self.indicer(k.labels_)
99 | features_group = self.make_subbags(indices,features)
100 |
101 | return features_group
102 |
103 | def embedding_grouping(self,features):
104 | features = features.squeeze()
105 | k = KMeans(n_clusters=self.groups_num, random_state=0,n_init='auto').fit(features.cpu().detach().numpy())
106 | indices = self.indicer(k.labels_)
107 | features_group = self.make_subbags(indices,features)
108 |
109 | return features_group
110 |
111 | def random_grouping(self, features):
112 | B, N, C = features.shape
113 | features = features.squeeze()
114 | indices = split_array(np.array(range(int(N))),self.groups_num)
115 | features_group = self.make_subbags(indices,features)
116 |
117 | return features_group
118 |
119 | def seqential_grouping(self, features):
120 | B, N, C = features.shape
121 | features = features.squeeze()
122 | indices = np.array_split(range(N),self.groups_num)
123 | features_group = self.make_subbags(indices,features)
124 |
125 | return features_group
126 |
127 | def idx_grouping(self,idx,features):
128 | idx = idx.cpu().numpy()
129 | idx = idx.reshape(-1)
130 | B, N, C = features.shape
131 | features = features.squeeze()
132 | indices = self.indicer(idx)
133 | features_group = self.make_subbags(indices,features)
134 |
135 | return features_group
136 |
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/utils/utils.py:
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1 | import pandas as pd
2 | import argparse
3 | import numpy as np
4 | from sklearn.cluster import KMeans
5 | import torch
6 |
7 | def make_parse():
8 | parser = argparse.ArgumentParser()
9 | parser.add_argument('--type', default='TCGA',type=str)
10 | parser.add_argument('--mode', default='random',type=str)
11 | parser.add_argument('--in_chans', default=1024,type=int)
12 | parser.add_argument('--num_subbags', default=16,type=int)
13 | parser.add_argument('--embed_dim', default=512,type=int)
14 | parser.add_argument('--attn', default='normal',type=str)
15 | parser.add_argument('--gm', default='cluster',type=str)
16 | parser.add_argument('--cls', default=True,type=bool)
17 | parser.add_argument('--num_msg', default=1,type=int)
18 | parser.add_argument('--ape', default=True,type=bool)
19 | parser.add_argument('--n_classes', default=2,type=int)
20 | parser.add_argument('--num_layers', default=2,type=int)
21 | parser.add_argument('--h5', default='./h5_dir',type=str)
22 | parser.add_argument('--csv', default='./data.csv',type=str)
23 | parser.add_argument('--seed', default=2087,type=int)
24 | parser.add_argument('--test', default=None,type=str)
25 | parser.add_argument('--num_test', default=10,type=int)
26 | args = parser.parse_args()
27 | return args
28 |
29 | def calculate_error(Y_hat, Y):
30 | error = 1. - Y_hat.float().eq(Y.float()).float().mean().item()
31 | return error
32 |
33 | def coords_nomlize(coords):
34 | coords = coords.squeeze()
35 | means = torch.mean(coords,0)
36 | xmean,ymean = means[0],means[1]
37 | stds = torch.std(coords,0)
38 | xstd,ystd = stds[0],stds[1]
39 | xcoords = (coords[:,0] - xmean)/xstd
40 | ycoords = (coords[:,1] - ymean)/ystd
41 | xcoords,ycoords = xcoords.view(xcoords.shape[0],1),ycoords.view(ycoords.shape[0],1)
42 | coords = torch.cat((xcoords,ycoords),dim=1)
43 | return coords
44 |
45 | def shuffle_msg(x):
46 | # (B, G, win**2+1, C)
47 | x = x.unsqueeze(dim=0)
48 | B, G, N, C = x.shape
49 | if G == 1:
50 | return x
51 | msges = x[:, :, 0] # (B, G, C)
52 | assert C % G == 0
53 | msges = msges.view(-1, G, G, C//G).transpose(1, 2).reshape(B, G, 1, C)
54 | print(msges.shape)
55 | x = torch.cat((msges, x[:, :, 1:]), dim=2)
56 | x = x.squeeze(dim=0)
57 | return x
58 |
59 | def padding(h):
60 | H = h.shape[1]
61 | _H, _W = int(np.ceil(np.sqrt(H))), int(np.ceil(np.sqrt(H)))
62 | add_length = _H * _W - H
63 | h = torch.cat([h, h[:,:add_length,:]],dim = 1)
64 | return h,_H,_W
65 |
66 | def cat_msg2cluster_group(x_groups,msg_tokens):
67 | x_groups_cated = []
68 | for x in x_groups:
69 | x = x.unsqueeze(dim=0)
70 | try:
71 | temp = torch.cat((msg_tokens,x),dim=2)
72 | except Exception as e:
73 | print('Error when cat msg tokens to sub-bags')
74 | x_groups_cated.append(temp)
75 |
76 | return x_groups_cated
77 |
78 |
79 |
80 | def split_array(array, m):
81 | n = len(array)
82 | indices = np.random.choice(n, n, replace=False)
83 | split_indices = np.array_split(indices, m)
84 |
85 | result = []
86 | for indices in split_indices:
87 | result.append(array[indices])
88 |
89 | return result
90 |
91 | class EarlyStopping:
92 | """Early stops the training if validation loss doesn't improve after a given patience."""
93 | def __init__(self, patience=20, stop_epoch=50, verbose=False):
94 | """
95 | Args:
96 | patience (int): How long to wait after last time validation loss improved.
97 | Default: 20
98 | stop_epoch (int): Earliest epoch possible for stopping
99 | verbose (bool): If True, prints a message for each validation loss improvement.
100 | Default: False
101 | """
102 | self.patience = patience
103 | self.stop_epoch = stop_epoch
104 | self.verbose = verbose
105 | self.counter = 0
106 | self.best_score = None
107 | self.early_stop = False
108 | self.val_loss_min = np.Inf
109 | self.flag = False
110 |
111 | def __call__(self, epoch, val_loss, model, args, ckpt_name = ''):
112 | ckpt_name = './ckp/{}_checkpoint_{}_{}.pt'.format(str(args.type),str(args.seed),str(epoch))
113 | score = -val_loss
114 | self.flag = False
115 | if self.best_score is None:
116 | self.best_score = score
117 | self.save_checkpoint(val_loss, model, ckpt_name, args)
118 | elif score < self.best_score:
119 | self.counter += 1
120 | print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
121 | if self.counter >= self.patience and epoch > self.stop_epoch:
122 | self.early_stop = True
123 | else:
124 | self.best_score = score
125 | self.save_checkpoint(val_loss, model, ckpt_name, args)
126 | self.counter = 0
127 |
128 |
129 | def save_checkpoint(self, val_loss, model, ckpt_name, args):
130 | '''Saves model when validation loss decrease.'''
131 | if self.verbose and not args.overfit:
132 | print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...',ckpt_name)
133 | elif self.verbose and args.overfit:
134 | print(f'Training loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...',ckpt_name)
135 | torch.save(model.state_dict(), ckpt_name)
136 | print(ckpt_name)
137 | self.val_loss_min = val_loss
138 | self.flag = True
139 |
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