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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/shenxiaochenn/SongCi

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Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning

https://github.com/shizhiceng/mclstexp

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Multimodal contrastive learning for spatial gene expression prediction using histology images

https://github.com/sigma10010/histo_img_seg

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Structure-aware scale-adaptive networks for cancer segmentation in whole-slide images

https://github.com/soham-chitnis10/WSI-domain-specific

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Domain-Specific Pre-training Improves Confidence in Whole Slide Image Classification

https://github.com/sophiajw/HistAuGAN

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Structure-Preserving Multi-Domain Stain Color Augmentation using Style-Transfer with Disentangled Representations

https://github.com/sulam-group/mil-pathology

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Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images

https://github.com/szc19990412/hvtsurv

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HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image

https://github.com/taimurhassan/cancer

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A Dilated Residual Hierarchically Fashioned Segmentation Framework for Extracting Gleason Tissues and Grading Prostate Cancer from Whole Slide Images

https://github.com/technioncomputationalmrilab/colorectal_cancer_ai

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Patient-level Microsatellite Stability Assessment from Whole Slide Images By Combining Momentum Contrast Learning and Group Patch Embeddings

https://github.com/technioncomputationalmrilab/msi_mss_bp-cnn

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Exploring the Interplay Between Colorectal Cancer Subtypes Genomic Variants and Cellular Morphology: A Deep-Learning Approach

https://github.com/tencent-ailab/rlogist

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RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement Learning

https://github.com/tio-ikim/valuing-vicinity

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Valuing Vicinity: Memory attention framework for context-based semantic segmentation in histopathology

https://github.com/tissueimageanalytics/cerberus

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One Model is All You Need: Multi-Task Learning Enables Simultaneous Histology Image Segmentation and Classification

https://github.com/tissueimageanalytics/conic

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CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

https://github.com/tissueimageanalytics/tiatoolbox

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TIAViz: A Browser-based Visualization Tool for Computational Pathology Models

https://github.com/trislaz/gigassl

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Giga-SSL: Self-Supervised Learning for Gigapixel Images

https://github.com/tueimage/gabmil

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A Spatially-Aware Multiple Instance Learning Framework for Digital Pathology

https://github.com/tueimage/mad-mil

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Multi-head Attention-based Deep Multiple Instance Learning

https://github.com/uit-hdl/bc_molsubtyping

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Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images

https://github.com/uit-hdl/histology

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A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images

https://github.com/uta-smile/DeepAttnMISL

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Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

https://github.com/vison307/mammil

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MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Models

https://github.com/vkola-lab/unaah

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U-Net-and-a-half: Convolutional network for biomedical image segmentation using multiple expert-driven annotations

https://github.com/vqdang/h2t

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Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Images

https://github.com/wenqi006/slidegraph

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SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancer

https://github.com/windygoo/pathcontext

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Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images

https://github.com/winterpan2017/proto2seg

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Human-machine Interactive Tissue Prototype Learning for Label-efficient Histopathology Image Segmentation

https://github.com/wollf2008/fw-rd

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Whole-slide-imaging Cancer Metastases Detection and Localization with Limited Tumorous Data

https://github.com/wonderlandxd/wikg

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Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis

https://github.com/xjtuai/histogym

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HistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis

https://github.com/xy-always/genequery

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GeneQuery: A General QA-based Framework for Spatial Gene Expression Predictions from Histology Images

https://github.com/y-research-sbu/otsurv

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OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport

https://github.com/yang-ze-kang/scmil

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SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images

https://github.com/yonischirris/ectil

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ECTIL: Label-efficient Computational Tumour Infiltrating Lymphocyte (TIL) assessment in breast cancer: Multicentre validation in 2,340 patients with breast cancer

https://github.com/yuankaihuo/EasierPath

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EasierPath: An Open-source Tool for Human-in-the-loop Deep Learning of Renal Pathology

https://github.com/yuanzhang7/dscenet

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DSCENet: Dynamic Screening and Clinical-Enhanced Multimodal Fusion for MPNs Subtype Classification

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