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Awesome Domain Generalization for Computational Pathology
https://github.com/mostafajahanifar/awesome-dg-cpath
List: awesome-dg-cpath
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Awesome Domain Generalization for Computational Pathology
- Host: GitHub
- URL: https://github.com/mostafajahanifar/awesome-dg-cpath
- Owner: mostafajahanifar
- Created: 2023-11-15T22:28:48.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-23T12:38:38.000Z (about 1 year ago)
- Last Synced: 2024-06-12T21:42:32.495Z (6 months ago)
- Size: 66.4 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - awesome-dg-cpath - Awesome Domain Generalization for Computational Pathology. (Other Lists / Monkey C Lists)
README
# Awesome Domain Generalization for Computational Pathology
[](https://awesome.re)
This repository contains lists of resources (including [Datasets](#open_file_folder-datasets) and [Code Bases](#computer-code-bases)) that can help domain generalization research in computational pathology. These resources and their related concepts are further explained in the following manuscript:
```
@misc{jahanifar2023domain,
title={Domain Generalization in Computational Pathology: Survey and Guidelines},
author={Mostafa Jahanifar and Manahil Raza and Kesi Xu and Trinh Vuong and Rob Jewsbury and Adam Shephard and Neda Zamanitajeddin and Jin Tae Kwak and Shan E Ahmed Raza and Fayyaz Minhas and Nasir Rajpoot},
year={2023},
eprint={2310.19656},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
Any contribution will be appreciated. To contribute to this awesome list or suggest new resources, please make a PR and add your suggestions.
## :open_file_folder: Datasets
Publicly available datasets for DG experiments in CPath. Column `DS` represents the type domain shift that can be studied with each dataset (`1: Covariate Shift`, `2: Prior Shift`, `3: Posterior Shift`, and `4: Class-Conditional Shift`).
| Dataset | Application/Task | DS | Domains |
|---------------------|---------------------------------------------------------|----|------------------------------------------|
| | **Detection** | | |
| ATYPIA14 [[paper](http://ludo17.free.fr/mitos_atypia_2014/icpr2014_MitosAtypia_DataDescription.pdf)][[download](https://mitos-atypia-14.grand-challenge.org/)] | Mitosis detection in breast cancer | 1 | 2 scanners |
| Crowdsource [[paper](https://www.worldscientific.com/doi/epdf/10.1142/9789814644730_0029)] | Nuclei detection in renal cell carcinoma | 3 | 6 annotators |
| TUPAC-Aux [[paper](https://www.sciencedirect.com/science/article/pii/S1361841518305231)][[download](https://tupac.grand-challenge.org/)] | Mitosis detection in breast cancer | 1 | 3 centers |
| DigestPath [[paper](https://www.sciencedirect.com/science/article/pii/S1361841522001323)][[download](https://digestpath2019.grand-challenge.org/)] | Signet ring cell detection in colon cancer | 1 | 4 centers |
| TiGER-Cells [[paper](https://arxiv.org/abs/2206.11943)][[download](https://tiger.grand-challenge.org/)] | TILs detection in breast cancer | 1 | 3 sources |
| EndoNuke [[paper](https://www.mdpi.com/2306-5729/7/6/75)][[download](https://endonuke.ispras.ru/)] | Nuclei Detection in Estrogen and Progesterone Stained IHC Endometrium Scans| 3 | 7 annotators |
| MIDOG [[paper]()][[download]()] | Mitosis detection in multiple cancer types | 1, 2, 3 | 7 tumors, 2 species |
| | **Classification** | | |
| TUPAC-Mitosis [[paper](https://www.sciencedirect.com/science/article/pii/S1361841518305231)][[download](https://tupac.grand-challenge.org/)] | BC proliferation scoring based on mitosis score | 1 | 3 centers |
| Camelyon16 [[paper](https://jamanetwork.com/journals/jama/article-abstract/2665774)][[download](https://camelyon16.grand-challenge.org/)] | Lymph node WSI classification for BC metastasis | 1 | 2 centers |
| PatchCamelyon [[paper](https://link.springer.com/chapter/10.1007/978-3-030-00934-2_24)][[download](https://patchcamelyon.grand-challenge.org/)] | BC tumor classification based on Camelyon16 | 1 | 2 centers |
| Camelyon17 [[paper](https://ieeexplore.ieee.org/abstract/document/8447230)][[download](https://camelyon17.grand-challenge.org/)] | BC metastasis detection and pN-stage estimation | 1 | 5 centers |
| LC25000 [[paper](https://arxiv.org/abs/1912.12142)][[download](https://github.com/tampapath/lung_colon_image_set)] | Lung and colon tumor classification | 4 | 2 organs |
| Kather 100K [[paper](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1002730)][[download](https://zenodo.org/records/1214456)] | Colon cancer tissue phenotype classification | 1 | 3 centers |
| WILDS [[paper](https://arxiv.org/abs/2012.07421)][[download](https://github.com/p-lambda/wilds)] | BC tumor classification based on Camelyon17 | 1 | 5 centers |
| HunCRC [[paper](https://www.nature.com/articles/s41597-022-01450-y)][[download](https://doi.org/10.6084/m9.figshare.c.5927795.v1)] | Screening status of colon cancer or normal tissue | 1, 4 | 4 polyps, 2 sampling |
| PANDA [[paper](https://www.nature.com/articles/s41591-021-01620-2)][[download](https://panda.grand-challenge.org/)] | ISUP and Gleason grading of prostate cancer | 1, 2, 3 | 2 centers |
| | **Regression** | | |
| TUPAC-PAM50 [[paper](https://www.sciencedirect.com/science/article/pii/S1361841518305231)][[download](https://tupac.grand-challenge.org/)] | BC proliferation scoring based on PAM50 | 1 | 3 centers |
| LYSTO [[paper](https://arxiv.org/abs/2301.06304)][[download](https://lysto.grand-challenge.org/)] | Lymphocyte assessment (counting) in IHC images | 1 | 3 cancers, 9 centers |
| CoNIC (Lizard) [[paper](https://warwick.ac.uk/fac/cross_fac/tia/data/)][[download](https://arxiv.org/abs/2108.11195)] | Cellular composition in colon cancer | 1, 3 | 6 sources |
| TiGER-TILs [[paper](https://arxiv.org/abs/2206.11943)][[download](https://tiger.grand-challenge.org/)] | TIL score estimation in breast cancer | 1 | 3 sources |
| | **Segmentation** | | |
| Crowdsource [[paper](https://www.worldscientific.com/doi/epdf/10.1142/9789814644730_0029)] | Nuclear segmentation in renal cell carcinoma | 3 | 6 annotators |
| Camelyon [[paper]()][[download]()] | BC metastasis segmentation in lymph node WSIs | 1 | 2 and 5 centers |
| DS Bowl 2018 [[paper](https://www.nature.com/articles/s41592-019-0612-7)][[download](https://www.kaggle.com/c/data-science-bowl-2018)] | Nuclear instance segmentation | 1, 4 | 31 sets, 5 modalities |
| CPM [[paper](https://arxiv.org/abs/1810.13230)][[download](https://drive.google.com/drive/folders/1l55cv3DuY-f7-JotDN7N5nbNnjbLWchK)] | Nuclear instance segmentation | 1, 4 | 4 cancers |
| BCSS [[paper](https://academic.oup.com/bioinformatics/article/35/18/3461/5307750)][[download](https://bcsegmentation.grand-challenge.org/)] | Semantic tissue segmentation in BC (from TCGA) | 1 | 20 centers |
| AIDPATH [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0169260719311381)] | Glomeruli segmentation in Kidney biopsies | 1 | 3 centers |
| PanNuke [[paper](https://arxiv.org/abs/2003.10778)][[download](https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke)] | Nuclear instance segmentation and classification | 1, 2, 4 | 19 organs |
| MoNuSeg [[paper](https://ieeexplore.ieee.org/document/8880654)][[download](https://monuseg.grand-challenge.org/)] | Nuclear instance segmentation in H&E images | 1 | 9 organs, 18 centers |
| CryoNuSeg [[paper](https://www.sciencedirect.com/science/article/pii/S0010482521001438)][[download](https://www.kaggle.com/datasets/ipateam/segmentation-of-nuclei-in-cryosectioned-he-images)] | Nuclear segmentation in cryosectioned H&E | 1, 3 | 10 organs, 3 annotations |
| MoNuSAC [[paper](https://ieeexplore.ieee.org/abstract/document/9446924)][[download](https://monusac-2020.grand-challenge.org/)] | Nuclear instance segmentation and classification | 1, 2 | 37 centers, 4 organs |
| Lizard [[paper](https://warwick.ac.uk/fac/cross_fac/tia/data/)][[download](https://arxiv.org/abs/2108.11195)] | Nuclear instance segmentation and classification | 1, 3 | 6 sources |
| MetaHistoSeg [[paper](https://arxiv.org/abs/2109.14754)][[download](https://github.com/salesforce/MetaHistoSeg)] | Multiple segmentation tasks in various cancers | 1 | 5 sources/tasks |
| PANDA [[paper](https://www.nature.com/articles/s41591-021-01620-2)][[download](https://panda.grand-challenge.org/)] | Tissue segmentation in prostate cancer | 1, 2 | 2 centers |
| TiGER-BCSS [[paper](https://arxiv.org/abs/2206.11943)][[download](https://tiger.grand-challenge.org/)] | Tissue segmentation in BC (BCSS extension) | 1 | 3 sources |
| DigestPath [[paper](https://www.sciencedirect.com/science/article/abs/pii/S1361841522001323)][[download](https://digestpath2019.grand-challenge.org/)] | Colon tissue segmentation | 1 | 4 centers |
| NuInsSeg [[paper](https://arxiv.org/abs/2308.01760)][[download](https://www.kaggle.com/datasets/ipateam/nuinsseg)] | Nuclear instance segmentation pan-cancer/species | 1,4 | 31 organs, 2 species |
| | **Survival and gene expression prediction** | | |
| TCGA [[papers](https://www.cancer.gov/ccg/research/genome-sequencing/tcga/publications)][[download](https://portal.gdc.cancer.gov/)] | Pan-cancer survival and gene expression prediction | 1, 2, 4 | 33 cancers, 20 centers |
| CPTAC [[papers](https://www.cell.com/consortium/cptac)][[download](https://gdc.cancer.gov/about-gdc/contributed-genomic-data-cancer-research/clinical-proteomic-tumor-analysis-consortium-cptac)][[tool](https://pypi.org/project/cptac/)] | Pan-cancer survival and gene expression prediction | 1, 2 | 10 cancers, 11 centers |
## :computer: Code bases
| Reference | DG Method | Title |
|-----------|-----------|-------|
| | **Pretraining** | |
| Yang *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841522001864)][[code](https://github.com/easonyang1996/CS-CO)] | Minimizing Contrastive Loss | CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images |
| Li *et al*. [[paper](https://conferences.miccai.org/2022/papers/293-Paper1939.html)][[code](https://github.com/junl21/lacl?utm_source=catalyzex.com)] | Minimizing Contrastive Loss | Lesion-Aware Contrastive Representation Learning For Histopathology Whole Slide Images Analysis |
| Galdran *et al*. [[paper](https://conferences.miccai.org/2022/papers/500-Paper1738.html)][[code](https://github.com/agaldran/t3po?utm_source=catalyzex.com)] | Unsupervised/Self-supervised learning | Test Time Transform Prediction for Open Set Histopathological Image Recognition |
| Bozorgtabar *et al*. [[paper](https://openaccess.thecvf.com/content/ICCV2021W/CVAMD/papers/Bozorgtabar_SOoD_Self-Supervised_Out-of-Distribution_Detection_Under_Domain_Shift_for_Multi-Class_Colorectal_ICCVW_2021_paper.pdf)][[code](https://github.com/BehzadBozorgtabar/SOoD?utm_source=catalyzex.com)] | Unsupervised/Self-supervised learning | SOoD: Self-Supervised Out-of-Distribution Detection Under Domain Shift for Multi-Class Colorectal Cancer Tissue Types |
| Koohbanani *et al*. [[paper](https://ieeexplore.ieee.org/abstract/document/9343323)][[code](https://github.com/navidstuv/self_path)] | Multiple Pretext Tasks | Self Path: Self Supervision for Classification of Histology Images with Limited Budget of Annotation |
| Abbet *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841522001207)][[code](https://github.com/christianabbet/SRA)] | Unsupervised/Self-supervised learning | Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection |
| Cho *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-030-87237-3_37)][[code](https://github.com/hyeonwoocho7/Cell_Detection-MICCAI)] | Unsupervised/Self-supervised learning | Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap |
| Chikontwe *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841522001293)][[code](https://github.com/PhilipChicco/wsshisto)] | Unsupervised/Self-supervised learning | Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization |
| Tran *et al*. [[paper](https://conferences.miccai.org/2022/papers/434-Paper0610.html)][[code](https://github.com/manuel-tran/s5cl)] | Minimizing Contrastive Loss | S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning |
| Sikaroudi *et al*. [[paper](https://ieeexplore.ieee.org/document/9176279)][[code](https://github.com/bghojogh/Siamese-Network-Histopathology)] | Unsupervised/Self-supervised learning | Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study |
| Wang *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841522002043)][[code](https://github.com/Xiyue-Wang/TransPath)] | Unsupervised/Self-supervised learning | Transformer-based unsupervised contrastive learning for histopathological image classification |
| Kang *et al*. [[paper](https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Benchmarking_Self-Supervised_Learning_on_Diverse_Pathology_Datasets_CVPR_2023_paper.html)][[code](https://github.com/lunit-io/benchmark-ssl-pathology)] | Unsupervised/Self-supervised learning | Benchmarking Self-Supervised Learning on Diverse Pathology Datasets |
| Lazard *et al*. [[paper](https://openaccess.thecvf.com/content/CVPR2023W/CVMI/papers/Lazard_Giga-SSL_Self-Supervised_Learning_for_Gigapixel_Images_CVPRW_2023_paper.pdf)][[code](https://github.com/trislaz/gigassl)] | Contrastive Learning | Giga-SSL: Self-Supervised Learning for Gigapixel Images |
| Vuong *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-031-25066-8_31)][[code](https://github.com/trinhvg/IMPash)] | Contrastive Learning | IMPaSh: A Novel Domain-Shift Resistant Representation for Colorectal Cancer Tissue Classification |
| Chen *et al*. [[paper](https://www.nature.com/articles/s41551-022-00929-8)][[code](https://github.com/mahmoodlab/SISH)] | Unsupervised/Self-supervised learning | Fast and scalable search of whole-slide images via self-supervised deep learning |
| | **Meta-Learning** | |
| Sikaroudi *et al*. [[paper](https://ieeexplore.ieee.org/document/9433978)][[code](https://github.com/bghojogh/Histopathology-Magnification-Generalization)] | Meta-learning | Magnification Generalization For Histopathology Image Embedding |
| Yuan *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-030-88210-5_27)][[code](https://github.com/salesforce/MetaHistoSeg)] | Meta-learning | MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation |
| | **Domain Alignment** | |
| Sharma *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-031-16434-7_34)][[code](https://github.com/YashSharma/MaNi)] | Mutual Information | MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation |
| Boyd *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-031-16434-7_35)][[code](https://github.com/jcboyd/miccai2022-roigan)] | Generative Models | Region-guided CycleGANs for Stain Transfer in Whole Slide Images |
| Kather *et al*. [[paper](https://www.nature.com/articles/s41591-019-0462-y)][[code](https://github.com/jnkather/MSIfromHE/tree/master)] | Stain Normalization | Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer |
| Zheng *et al*. [[paper](https://www.sciencedirect.com/science/article/abs/pii/S0169260718312161)][[code](https://github.com/Zhengyushan/adaptive_color_deconvolution)] | Stain Normalization | Adaptive color deconvolution for histological WSI normalization |
| Sebai *et al*. [[paper](https://link.springer.com/article/10.1007/s11517-020-02175-z)][[code](https://github.com/MeriemSebai/MaskMitosis)] | Stain Normalization | MaskMitosis: a deep learning framework for fully supervised, weakly supervised, and unsupervised mitosis detection in histopathology images |
| Zhang *et al*. [[paper](https://arxiv.org/abs/2206.12505)][[code](https://github.com/BzhangURU/Paper_2022_Co-training)] | Minimizing Contrastive Loss | Stain Based Contrastive Co-training for Histopathological Image Analysis |
| Shahban *et al*. [[paper](https://arxiv.org/abs/1804.01601)][[code](https://github.com/xtarx/StainGAN)] | Generative Models | Staingan: Stain Style Transfer for Digital Histological Images |
| Wagner *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-031-16434-7_2)][[code](https://github.com/meclabtuda/bottlegan)] | Generative Models | Federated Stain Normalization for Computational Pathology|
| Quiros *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-030-87237-3_58)][[code](https://github.com/AdalbertoCq/Adversarial-learning-of-cancer-tissue-representations)] | Domain Adversarial Learning | Adversarial learning of cancer tissue representations |
| Salehi *et al*. [[paper](https://conferences.miccai.org/2022/papers/540-Paper2464.html)][[code](https://github.com/marrlab/AE-CFE)] | Minimizing the KL Divergence | Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification |
| Wilm *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-030-97281-3_1)][[code](https://github.com/DeepMicroscopy/MIDOG)] | Domain-Adversarial Learning | Domain adversarial retinanet as a reference algorithm for the mitosis domain generalization (midog) challenge |
| Haan *et al*. [[paper](https://www.nature.com/articles/s41467-021-25221-2)][[code](https://github.com/kevindehaan/stain-transformation)] | Generative models | Deep learning-based transformation of H&E stained tissues into special stains |
| Dawood *et al*. [[paper](https://openreview.net/forum?id=flfJ1OwD-FD)][[code](https://github.com/pkeller00/Src-Site-Pred)] | Stain Normalization | Do Tissue Source Sites leave identifiable Signatures in Whole Slide Images beyond staining? |
| | **Data Augmentation** | |
| Pohjonen *et al*. [[paper](https://arxiv.org/pdf/2206.15274.pdf)][[code](https://github.com/jopo666/StrongAugment)] | Data augmentation | Augment like there’s no tomorrow: Consistently performing neural networks for medical imaging |
| Chang *et al*. [[paper](https://miccai2021.org/openaccess/paperlinks/2021/09/01/453-Paper0297.html)][[code](https://github.com/aetherAI/stain-mixup)] | Stain Augmentation | Stain Mix-up: Unsupervised Domain Generalization for Histopathology Images |
| Shen *et al*. [[paper](https://conferences.miccai.org/2022/papers/406-Paper1231.html)][[code](https://github.com/yiqings/RandStainNA)] | Stain Augmentation | RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization |
| Koohbanani *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841520301353)][[code](https://github.com/mostafajahanifar/nuclick_torch)] | Data augmentation | NuClick: A deep learning framework for interactive segmentation of microscopic images |
| Wang *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841522003310)][[code](https://github.com/Xiyue-Wang/1st-in-MICCAI-MIDOG-2021-challenge)] | Data augmentation | A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images |
| Lin *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-031-16434-7_14)][[code](https://github.com/hust-linyi/insmix)] | Generative Models | InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation |
| Zhang *et al*. [[paper](https://rdcu.be/cVRrG)][[code](https://github.com/superjamessyx/robustness_benchmark)] | Data augmentation | Benchmarking the Robustness of Deep Neural Networks to Common Corruptions in Digital Pathology |
| Yamashita *et al*. [[paper](https://ieeexplore.ieee.org/abstract/document/9503389)][[code](https://github.com/rikiyay/style-transfer-for-digital-pathology)] | Style Transfer Models | Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation |
| Falahkheirkhah *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S002368372200006X)][[code](https://github.com/kiakh93/Synthesizing-histological-images)] | Generative Models | Deepfake Histologic Images for Enhancing Digital Pathology |
| Scalbert *et al*. [[paper](https://conferences.miccai.org/2022/papers/503-Paper0733.html)][[code](https://gitlab.com/vitadx/articles/test-time-i2i-translation-ensembling)] | Generative Models | Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology |
| Mahmood *et al*. [[paper](https://ieeexplore.ieee.org/abstract/document/8756037)][[code](https://github.com/mahmoodlab/NucleiSegmentation)] | Generative Models | Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images |
| Fan *et al*. [[paper](https://conferences.miccai.org/2022/papers/209-Paper2730.html)][[code](https://github.com/hellodfan/fastFF2FFPE)] | Generative Models | Fast FF-to-FFPE Whole Slide Image Translation via Laplacian Pyramid and Contrastive Learning |
| Marini *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S2153353922007830)][[code](https://github.com/ilmaro8/Data_Driven_Color_Augmentation)] | Stain Augmentation | Data-driven color augmentation for H&E stained images in computational pathology |
| Faryna *et al*. [[paper](https://proceedings.mlr.press/v143/faryna21a.html)][[code](https://github.com/DIAGNijmegen/pathology-he-auto-augment)] | RandAugment for Histology | Tailoring automated data augmentation to H&E-stained histopathology |
| | **Model Design** | |
| Graham *et al*. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9153847)][[code](https://github.com/simongraham/dsf-cnn)] | Model design | Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images |
| Lafarge *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841520302139)][[code](https://github.com/tueimage/se2cnn)] | Model design | Roto-translation equivariant convolutional networks: Application to histopathology image analysis |
| Zhang *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841522000676)][[code](https://github.com/ZhangXX54/DDTNet)] | Model design | DDTNet: A dense dual-task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer |
| Graham *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841522003139)][[code](https://github.com/TissueImageAnalytics/cerberus)] | Model Design | One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification |
| Yu *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841523000099)][[code](https://github.com/Zero-We/PMIL)] | Model Design | Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images|
| Yaar *et al*. [[paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9151061)][[code](https://github.com/asfandasfo/LUPI)] | Model Design | Cross-Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients |
| Tang *et al*. [[paper](https://ieeexplore.ieee.org/document/9607807)][[code](https://github.com/mahdihosseini/DARTS-ADP)] | Model Design | Probeable DARTS with Application to Computational Pathology |
| Vuong *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841521002516)][[code](https://github.com/trinhvg/JCO_Learning-pytorch)] | Model Design | Joint categorical and ordinal learning for cancer grading in pathology images |
| | **Domain Separation** | |
| Wagner *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-030-87237-3_25)][[code](https://github.com/sophiajw/HistAuGAN)] | Generative Models | HistAuGAN: Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations |
| Chikontwe *et al*. [[paper](https://link.springer.com/chapter/10.1007/978-3-031-16434-7_41)][[code](https://github.com/PhilipChicco/FRMIL)] | Learning disentangled representations | Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification |
| | **Ensemble Learning** | |
| Sohail *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841521001675)][[code](https://github.com/PRLAB21/Mitosis-Detection)] | Ensemble learning | Mitotic nuclei analysis in breast cancer histopathology images using deep ensemble classifier |
| | **Regularization Strategies** | |
| Mehrtens *et al*. [[paper](https://arxiv.org/abs/2301.01054)][[code](https://github.com/DBO-DKFZ/uncertainty-benchmark)] | Regularization Strategies | Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise |
| | **Other** | |
| Lu *et al*. [[paper](https://www.sciencedirect.com/science/article/pii/S1361841521003431)][[code](https://github.com/mahmoodlab/HistoFL)] | Other | Federated learning for computational pathology on gigapixel whole slide images |
| Aubreville *et al*. [[paper](https://arxiv.org/abs/2103.16515)][[code](https://github.com/DeepMicroscopy/MIDOG)] | Other | Quantifying the Scanner-Induced Domain Gap in Mitosis Detection |
| Sadafi *et al*. [[paper](https://arxiv.org/abs/2308.12679)][[code](https://github.com/marrlab/UACL)] | Other | A Continual Learning Approach for Cross-Domain White Blood Cell Classification |