IMAML-IDCG: Gradient-Based Meta-Learning with ImageNet Feature Reusing for Few-Shot Invasive Ductal Carcinoma Grading

This repo contains the reference source code for the paper [IMAML-IDCG: Gradient-Based Meta-Learning with ImageNet Feature Reusing for Few-Shot Invasive Ductal Carcinoma Grading]

Citation

If you find our code useful, please consider citing our work using the bibtex:

Enviroment

• Google Colab
• Google Drive
• Python3
• Pytorch
• json

Getting started

Clone the Repo

• Clone the repo into your Google Colab working directory

!git clone https://github.com/wingatesv/IMAML-IDCG.git

Datasets Download

• Please contact the author for more information: wingatesvoon@1utar.my

BreaKHis Class	4X	10X	20X	40X
Adenosis	114	113	111	106
Phyllodes Tumor	149	150	140	130
Tubular Adenoma	109	121	108	115
Lobular Carcinoma	156	170	163	137
Papillary Carcinoma	145	142	135	138
Mucinous Carcinoma	205	222	196	169
Fibroadenoma	253	260	264	237
Ductal Carcinoma	864	903	896	788
Total	1995	2081	2013	1820

BCHI Class	40X
Grade 1	98
Grade 2	102
Grade 3	82

PathoIDC Class	20X	40X
Grade 1	600	317
Grade 2	641	480
Grade 3	1245	361

Split	Dataset	link
Base (BC Classification)	BreaKHis_4x	https://drive.google.com/file/d/1CWRzSFRV1WsRyA_g8yYQvIQGs_QnUyYR/view?usp=sharing
	BreaKHis_10x	https://drive.google.com/file/d/1-5PuA8i4vmUN1fHAH8pveEjhLn5S5ACU/view?usp=sharing
	BreaKHis_20x	https://drive.google.com/file/d/1-JcLWy-cFmd7LVdcKaXPwJK-ox3_r5m2/view?usp=share_link
	BreaKHis_40x	https://drive.google.com/file/d/1yDbX4Nm39zOag4jqLeoxeWSQ3SeAZ0Aa/view?usp=share_link
Novel (IDC Grading)	BCHI	https://drive.google.com/file/d/1ssUKwT2jByxMPQahS58TFH_nga2yQ9D1/view?usp=share_link
	PathoIDC_20x	https://drive.google.com/file/d/1dXuY1TePiwcrsrvFW-I3-8CidaVXmgMx/view?usp=share_link
	PathoIDC_40x	https://drive.google.com/file/d/1-J6Hw2MZP5Grnqfgpl1-ZNk_pCGJWtyI/view?usp=share_link

BreaKHis_4x

• Change directory to ./filelists/BreaKHis_4x
• run source ./get_BreaKHis_4x.sh

BreaKHis_10x

• Change directory to ./filelists/BreaKHis_10x
• run source ./get_BreaKHis_10x.sh

BreaKHis_20x

• Change directory to ./filelists/BreaKHis_20x
• run source ./get_BreaKHis_20x.sh

BreaKHis_40x

• Change directory to ./filelists/BreaKHis_40x
• run source ./get_BreaKHis_40x.sh

BCHI

• Change directory to ./filelists/BCHI
• run source ./get_BCHI.sh

PathoIDC_20x

• Change directory to ./filelists/PathoIDC_20x
• run source ./get_PathoIDC_20x.sh

PathoIDC_40x

• Change directory to ./filelists/PathoIDC_40x
• run source ./get_PathoIDC_40x.sh

BreaKHis_{}X->BCHI

• Finish preparation for BreaKHis and BCHI and you are done!

BreaKHis_{}x->PathoIDC{}x

• Finish preparation for BreaKHis and PathoIDC and you are done!

Self-defined setting

• • Require one data split json file: 'base.json' for each BreaKHis dataset
• • Require two data split json file: 'val.json', 'novel.json' for BCHI and PathoIDC datasets
• The format should follow
  {"label_names": ["class0","class1",...], "image_names": ["filepath1","filepath2",...],"image_labels":[l1,l2,l3,...]}
• For example, run source ./get_BCHI_filelist.py to generate json files for the BCHI dataset
• Put these file in the same folder and change data_dir['DATASETNAME'] in configs.py to the folder path

Cross-Domain Configurations

Name	Description
cross_IDC_4x	BreaKHis_4x to BCHI
cross_IDC_10x	BreaKHis_10x to BCHI
cross_IDC_20x	BreaKHis_20x to BCHI
cross_IDC_40x	BreaKHis_40x to BCHI
cross_IDC_4x_2	BreaKHis_4x to PathoIDC_40x
cross_IDC_10x_2	BreaKHis_10x to PathoIDC_40x
cross_IDC_20x_2	BreaKHis_20x to PathoIDC_40x
cross_IDC_40x_2	BreaKHis_40x to PathoIDC_40x
cross_IDC_4x_3	BreaKHis_4x to PathoIDC_20x
cross_IDC_10x_3	BreaKHis_10x to PathoIDC_20x
cross_IDC_20x_3	BreaKHis_20x to PathoIDC_20x
cross_IDC_40x_3	BreaKHis_40x to PathoIDC_20x

Train

Run python ./train.py --dataset [DATASETNAME] --model [BACKBONENAME] --method [METHODNAME] [--OPTIONARG]

For example, run python ./train.py --dataset cross_IDC_40x --model ResNet34 --method imaml_idcg --train_n_way 3 --test_n_way 3 --n_shot 1 --stop_epoch 100 --train_aug --sn stainnet
Commands below follow this example, and please refer to io_utils.py for additional options.

Save features

Save the extracted feature before the classifaction layer to increase test speed. This is not applicable to MAML-based methods, but are required for other methods. Run python ./save_features.py --dataset cross_IDC_40x --model ResNet34 --method relationnet --train_n_way 3 --n_shot 5 --test_n_way 3 --train_aug --sn stainnet

Test

Run python ./test.py --dataset cross_IDC_40x --model ResNet34 --method imaml_idcg --train_n_way 3 --test_n_way 3 --n_shot 1 --train_aug --sn stainnet

Results

• The test results will be recorded in ./record/results.txt

References

• Main Framework https://github.com/wyharveychen/CloserLookFewShot
• Framework, Backbone, Method: Matching Network https://github.com/facebookresearch/low-shot-shrink-hallucinate
• Omniglot dataset, Method: Prototypical Network https://github.com/jakesnell/prototypical-networks
• Method: Relational Network https://github.com/floodsung/LearningToCompare_FSL
• Method: MAML https://github.com/cbfinn/maml
  https://github.com/dragen1860/MAML-Pytorch
  https://github.com/katerakelly/pytorch-maml