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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/SarderLab/tf-WSI-dataset-utils

공식 코드

Histo-fetch -- On-the-fly processing of gigapixel whole slide images simplifies and speeds neural network training

https://github.com/TIA-Lab/PanNuke-metrics

공식 코드

PanNuke Dataset Extension, Insights and Baselines

https://github.com/Vadori/CytoArk

공식 코드

Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and Classification

https://github.com/WinnieLaugh/CONIC_Pathology_AI

공식 코드

Keep It Accurate and Robust: An Enhanced Nuclei Analysis Framework

https://github.com/Xiyue-Wang/RetCCL

공식 코드

Analysis and Validation of Image Search Engines in Histopathology

https://github.com/YashSharma/C2C

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Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification

https://github.com/ZeyuGaoAi/Instance_based_Vision_Transformer

공식 코드

Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image

https://github.com/abhijeetptl5/historoi

공식 코드

Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training

https://github.com/abhijeetptl5/wsisegqc

공식 코드

Semantic Segmentation Based Quality Control of Histopathology Whole Slide Images

https://github.com/adalbertocq/histomorphological-phenotype-learning

공식 코드

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides

https://github.com/adamshephard/tiager

공식 코드

An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer

https://github.com/agaldran/kpis

공식 코드

KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to Slide-Level

https://github.com/agentdr1/la_mil

공식 코드

Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction

https://github.com/aican-research/breast-epithelium-segmentation

공식 코드

Immunohistochemistry guided segmentation of benign epithelial cells, in situ lesions, and invasive epithelial cells in breast cancer slides

https://github.com/aimlab-ubc/grasp

공식 코드

GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation

https://github.com/ajv012/survpath

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Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction

https://github.com/amayags/mustang

공식 코드

MUSTANG: Multi-Stain Self-Attention Graph Multiple Instance Learning Pipeline for Histopathology Whole Slide Images

https://github.com/andreped/NoCodeSeg

공식 코드

Code-free development and deployment of deep segmentation models for digital pathology

https://github.com/anonymized-for-review/automatic_meningioma_grading

공식 코드

Automatic and explainable grading of meningiomas from histopathology images

https://github.com/antoinehabis/wss-uic

공식 코드

Scribble-based fast weak-supervision and interactive corrections for segmenting whole slide images

https://github.com/aramis-lab/miccai2022-stratifiad

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Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer's Disease using weakly annotated whole slide histopathological images

https://github.com/atlasanalyticslab/pathbt

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Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnostics

https://github.com/atlasanalyticslab/vim4path

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Vim4Path: Self-Supervised Vision Mamba for Histopathology Images

https://github.com/axanderssonuu/abmil_with_accumulating_gradients

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End-to-end Multiple Instance Learning with Gradient Accumulation

https://github.com/baidu-research/NCRF

공식 코드

Cancer Metastasis Detection With Neural Conditional Random Field

https://github.com/basitalawode/mr-plip

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Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation

https://github.com/bearcleverproud/awesomewsi

공식 코드

A Survey of Pathology Foundation Model: Progress and Future Directions

https://github.com/bearcleverproud/mome

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MoME: Mixture of Multimodal Experts for Cancer Survival Prediction

https://github.com/benbergner/ips

공식 코드

Iterative Patch Selection for High-Resolution Image Recognition

https://github.com/beneroth13/dinov2

공식 코드

Low-resource finetuning of foundation models beats state-of-the-art in histopathology

https://github.com/bghojogh/Siamese-Network-Histopathology

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Supervision and Source Domain Impact on Representation Learning: A Histopathology Case Study

https://github.com/bianhao123/mixed_supervision

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Multiple Instance Learning with Mixed Supervision in Gleason Grading

https://github.com/bigyehahaha/m4

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M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis

https://github.com/binging512/icfnet

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ICFNet: Integrated Cross-modal Fusion Network for Survival Prediction

https://github.com/binli123/dsmil-wsi

공식 코드

Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning

https://github.com/biomedical-data-analysis-laboratory/histoprognostics

공식 코드

Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images

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