Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

awesome-dg-cpath

Awesome Domain Generalization for Computational Pathology
https://github.com/mostafajahanifar/awesome-dg-cpath

  • [paper - atypia-14.grand-challenge.org/)] | Mitosis detection in breast cancer | 1 | 2 scanners |
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  • [paper - challenge.org/)] | Mitosis detection in breast cancer | 1 | 3 centers |
  • [paper - challenge.org/)] | Signet ring cell detection in colon cancer | 1 | 4 centers |
  • [paper - challenge.org/)] | TILs detection in breast cancer | 1 | 3 sources |
  • [paper - challenge.org/)] | BC proliferation scoring based on mitosis score | 1 | 3 centers |
  • [paper - challenge.org/)] | Lymph node WSI classification for BC metastasis | 1 | 2 centers |
  • [paper - challenge.org/)] | BC tumor classification based on Camelyon16 | 1 | 2 centers |
  • [paper - challenge.org/)] | BC metastasis detection and pN-stage estimation | 1 | 5 centers |
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  • [paper - lambda/wilds)] | BC tumor classification based on Camelyon17 | 1 | 5 centers |
  • [paper - challenge.org/)] | ISUP and Gleason grading of prostate cancer | 1, 2, 3 | 2 centers |
  • [paper - challenge.org/)] | BC proliferation scoring based on PAM50 | 1 | 3 centers |
  • [paper - challenge.org/)] | Lymphocyte assessment (counting) in IHC images | 1 | 3 cancers, 9 centers |
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  • [paper - challenge.org/)] | TIL score estimation in breast cancer | 1 | 3 sources |
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  • [paper - science-bowl-2018)] | Nuclear instance segmentation | 1, 4 | 31 sets, 5 modalities |
  • [paper - f7-JotDN7N5nbNnjbLWchK)] | Nuclear instance segmentation | 1, 4 | 4 cancers |
  • [paper - challenge.org/)] | Semantic tissue segmentation in BC (from TCGA) | 1 | 20 centers |
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  • [paper - challenge.org/)] | Nuclear instance segmentation in H&E images | 1 | 9 organs, 18 centers |
  • [paper - of-nuclei-in-cryosectioned-he-images)] | Nuclear segmentation in cryosectioned H&E | 1, 3 | 10 organs, 3 annotations |
  • [paper - 2020.grand-challenge.org/)] | Nuclear instance segmentation and classification | 1, 2 | 37 centers, 4 organs |
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  • [paper - challenge.org/)] | Tissue segmentation in prostate cancer | 1, 2 | 2 centers |
  • [paper - challenge.org/)] | Tissue segmentation in BC (BCSS extension) | 1 | 3 sources |
  • [paper - challenge.org/)] | Colon tissue segmentation | 1 | 4 centers |
  • [paper - cancer/species | 1,4 | 31 organs, 2 species |
  • [papers - cancer survival and gene expression prediction | 1, 2, 4 | 33 cancers, 20 centers |
  • [papers - 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 |
  • [paper - CO)] | Minimizing Contrastive Loss | CS-CO: A Hybrid Self-Supervised Visual Representation Learning Method for H&E-stained Histopathological Images |
  • [paper - Aware Contrastive Representation Learning For Histopathology Whole Slide Images Analysis |
  • [paper - supervised learning | Test Time Transform Prediction for Open Set Histopathological Image Recognition |
  • [paper - supervised learning | SOoD: Self-Supervised Out-of-Distribution Detection Under Domain Shift for Multi-Class Colorectal Cancer Tissue Types |
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  • [paper - supervised learning | Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection |
  • [paper - MICCAI)] | Unsupervised/Self-supervised learning | Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap |
  • [paper - supervised learning | Weakly supervised segmentation on neural compressed histopathology with self-equivariant regularization |
  • [paper - tran/s5cl)] | Minimizing Contrastive Loss | S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning |
  • [paper - Network-Histopathology)] | Unsupervised/Self-supervised learning | Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study |
  • [paper - Wang/TransPath)] | Unsupervised/Self-supervised learning | Transformer-based unsupervised contrastive learning for histopathological image classification |
  • [paper - io/benchmark-ssl-pathology)] | Unsupervised/Self-supervised learning | Benchmarking Self-Supervised Learning on Diverse Pathology Datasets |
  • [paper - SSL: Self-Supervised Learning for Gigapixel Images |
  • [paper - Shift Resistant Representation for Colorectal Cancer Tissue Classification |
  • [paper - supervised learning | Fast and scalable search of whole-slide images via self-supervised deep learning |
  • [paper - Magnification-Generalization)] | Meta-learning | Magnification Generalization For Histopathology Image Embedding |
  • [paper - learning | MetaHistoSeg: A Python Framework for Meta Learning in Histopathology Image Segmentation |
  • [paper - Domain Unsupervised Segmentation |
  • [paper - roigan)] | Generative Models | Region-guided CycleGANs for Stain Transfer in Whole Slide Images |
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  • [paper - training)] | Minimizing Contrastive Loss | Stain Based Contrastive Co-training for Histopathological Image Analysis |
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  • [paper - learning-of-cancer-tissue-representations)] | Domain Adversarial Learning | Adversarial learning of cancer tissue representations |
  • [paper - CFE)] | Minimizing the KL Divergence | Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification |
  • [paper - Adversarial Learning | Domain adversarial retinanet as a reference algorithm for the mitosis domain generalization (midog) challenge |
  • [paper - transformation)] | Generative models | Deep learning-based transformation of H&E stained tissues into special stains |
  • [paper - Site-Pred)] | Stain Normalization | Do Tissue Source Sites leave identifiable Signatures in Whole Slide Images beyond staining? |
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  • [paper - mixup)] | Stain Augmentation | Stain Mix-up: Unsupervised Domain Generalization for Histopathology Images |
  • [paper - Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization |
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  • [paper - Wang/1st-in-MICCAI-MIDOG-2021-challenge)] | Data augmentation | A generalizable and robust deep learning algorithm for mitosis detection in multicenter breast histopathological images |
  • [paper - linyi/insmix)] | Generative Models | InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation |
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  • [paper - transfer-for-digital-pathology)] | Style Transfer Models | Learning domain-agnostic visual representation for computational pathology using medically-irrelevant style transfer augmentation |
  • [paper - histological-images)] | Generative Models | Deepfake Histologic Images for Enhancing Digital Pathology |
  • [paper - time-i2i-translation-ensembling)] | Generative Models | Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology |
  • [paper - Organ Nuclei Segmentation in Histopathology Images |
  • [paper - to-FFPE Whole Slide Image Translation via Laplacian Pyramid and Contrastive Learning |
  • [paper - driven color augmentation for H&E stained images in computational pathology |
  • [paper - he-auto-augment)] | RandAugment for Histology | Tailoring automated data augmentation to H&E-stained histopathology |
  • [paper - cnn)] | Model design | Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images |
  • [paper - translation equivariant convolutional networks: Application to histopathology image analysis |
  • [paper - task network for tumor-infiltrating lymphocyte detection and segmentation in histopathological images of breast cancer |
  • [paper - task learning enables simultaneous histology image segmentation and classification |
  • [paper - We/PMIL)] | Model Design | Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images|
  • [paper - Domain Knowledge Transfer for Prediction of Chemosensitivity in Ovarian Cancer Patients |
  • [paper - ADP)] | Model Design | Probeable DARTS with Application to Computational Pathology |
  • [paper - pytorch)] | Model Design | Joint categorical and ordinal learning for cancer grading in pathology images |
  • [paper - Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations |
  • [paper - calibration based Multiple Instance Learning for Whole Slide Image Classification |
  • [paper - Detection)] | Ensemble learning | Mitotic nuclei analysis in breast cancer histopathology images using deep ensemble classifier |
  • [paper - DKFZ/uncertainty-benchmark)] | Regularization Strategies | Benchmarking common uncertainty estimation methods with histopathological images under domain shift and label noise |
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  • [paper - Induced Domain Gap in Mitosis Detection |
  • [paper - Domain White Blood Cell Classification |