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whole slide images

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/deepmicroscopy/prototype-sampling

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Leveraging image captions for selective whole slide image annotation

https://github.com/deeppathology/caninecutaneoustumors

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Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

https://github.com/deeppathology/cox_amil

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Attention-based Multiple Instance Learning for Survival Prediction on Lung Cancer Tissue Microarrays

https://github.com/diagnijmegen/tumor-percentage-mil-regression

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"No negatives needed": weakly-supervised regression for interpretable tumor detection in whole-slide histopathology images

https://github.com/dylan-h-wang/dsf-wsi

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A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images

https://github.com/eduardchelebian/histology-umap

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Seeded iterative clustering for histology region identification

https://github.com/engrodawood/hist-mmd

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Maximum Mean Discrepancy Kernels for Predictive and Prognostic Modeling of Whole Slide Images

https://github.com/episphere/imagebox3

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ImageBox3: No-Server Tile Serving to Traverse Whole Slide Images on the Web

https://github.com/fuchs-lab-public/cpath_sabenchmark

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Benchmarking Embedding Aggregation Methods in Computational Pathology: A Clinical Data Perspective

https://github.com/fukexue/fast

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification

https://github.com/gexinh/bpgt

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Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification

https://github.com/giacomo-cgn/pdl1-wsi-classification

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PD-L1 Classification of Weakly-Labeled Whole Slide Images of Breast Cancer

https://github.com/hanminghao/h2gt

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Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Images

https://github.com/hanminghao/mscpt

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MSCPT: Few-shot Whole Slide Image Classification with Multi-scale and Context-focused Prompt Tuning

https://github.com/hanminghao/umpire

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Towards Unified Molecule-Enhanced Pathology Image Representation Learning via Integrating Spatial Transcriptomics

https://github.com/hhi-aml/histobenchmark

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From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology

https://github.com/histai/hibou

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Hibou: A Family of Foundational Vision Transformers for Pathology

https://github.com/histograph31/pix2pathology

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From Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis

https://github.com/hku-medai/higt

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HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis

https://github.com/hoarjour/histcode

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Contrastive learning-based computational histopathology predict differential expression of cancer driver genes

https://github.com/hongliuuuuu/wsi-sam

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WSI-SAM: Multi-resolution Segment Anything Model (SAM) for histopathology whole-slide images

https://github.com/hrlblab/AFM_kidney_cells

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How Good Are We? Evaluating Cell AI Foundation Models in Kidney Pathology with Human-in-the-Loop Enrichment

https://github.com/hrlblab/IRS

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IRS: Incremental Relationship-guided Segmentation for Digital Pathology

https://github.com/hrlblab/cs-mil

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Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

https://github.com/hrlblab/glo-in-one_v2

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Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse Histopathology

https://github.com/hrlblab/map3d

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An End-to-end Pipeline for 3D Slide-wise Multi-stain Renal Pathology Registration

https://github.com/hrlblab/molecularel

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Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning

https://github.com/hrlblab/spatial_pathomics

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Spatial Pathomics Toolkit for Quantitative Analysis of Podocyte Nuclei with Histology and Spatial Transcriptomics Data in Renal Pathology

https://github.com/hrzhang1123/dtfd-mil

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DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

https://github.com/huuquan1994/wsi_glomerulus_seg

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A General Pipeline for Glomerulus Whole-Slide Image Segmentation

https://github.com/hvcl/Clinical-grade-Pathology-Report-Generation

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Clinical-grade Multi-Organ Pathology Report Generation for Multi-scale Whole Slide Images via a Semantically Guided Medical Text Foundation Model

https://github.com/iam-cvc/helicobacterdetection

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Diagnosising Helicobacter pylori using AutoEncoders and Limited Annotations through Anomalous Staining Patterns in IHC Whole Slide Images

https://github.com/iamownt/TRUECAM

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Implementing Trust in Non-Small Cell Lung Cancer Diagnosis with a Conformalized Uncertainty-Aware AI Framework in Whole-Slide Images

https://github.com/ilmaro8/HE_adversarial_CNN

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H&E-adversarial network: a convolutional neural network to learn stain-invariant features through Hematoxylin & Eosin regression

https://github.com/ilmaro8/wsi_analysis

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Automatic Labels are as Effective as Manual Labels in Biomedical Images Classification with Deep Learning

https://github.com/imagingdatacommons/idc-comppath-reproducibility

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The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.