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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/innse/motcat

공식 코드

Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Prediction

https://github.com/irisdigitalpathology/iris-codec

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The Iris File Extension

https://github.com/irisdigitalpathology/iris-example-files

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The Iris File Extension

https://github.com/isyangshu/mambamil

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MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathology

https://github.com/jaiprakash1824/path_vis

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Beyond the Monitor: Mixed Reality Visualization and AI for Enhanced Digital Pathology Workflow

https://github.com/jamesdolezal/biscuit

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Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

https://github.com/jamesdolezal/slideflow

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Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

https://github.com/jarartur/histopathologyaugmentationresearch

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Improving Quality Control of Whole Slide Images by Explicit Artifact Augmentation

https://github.com/jcboyd/miccai2022-roigan

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Region-guided CycleGANs for Stain Transfer in Whole Slide Images

https://github.com/joesu666/siimil

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Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images

https://github.com/jsh0792/mcti

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Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images

https://github.com/jsliam94/cmswinkan

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Towards Accurate and Interpretable Neuroblastoma Diagnosis via Contrastive Multi-scale Pathological Image Analysis

https://github.com/junl21/lacl

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Lesion-Aware Contrastive Representation Learning for Histopathology Whole Slide Images Analysis

https://github.com/junlinguo/cellpose-inference-gpu

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Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology

https://github.com/junlinguo/cellvit-kidney

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Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology

https://github.com/junlinguo/stardist-inference-gpu

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Assessment of Cell Nuclei AI Foundation Models in Kidney Pathology

https://github.com/k-stacke/representation-shift

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A Closer Look at Domain Shift for Deep Learning in Histopathology

https://github.com/kaczmarj/hippo

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Explainable AI for computational pathology identifies model limitations and tissue biomarkers

https://github.com/kaiko-ai/eva

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Towards Large-Scale Training of Pathology Foundation Models

https://github.com/kaiko-ai/towards_large_pathology_fms

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Towards Large-Scale Training of Pathology Foundation Models

https://github.com/katalip/ca-stinv-cnn

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Improving Stain Invariance of CNNs for Segmentation by Fusing Channel Attention and Domain-Adversarial Training

https://github.com/khanhha/crack_segmentation

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Manifolds for Unsupervised Visual Anomaly Detection

https://github.com/konst-int-i/healnet

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HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data

https://github.com/layer6ai-labs/ProxyFL

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Decentralized Federated Learning through Proxy Model Sharing

https://github.com/lazytkm/prdl

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Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis

https://github.com/lely475/ocelot23algo

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SoftCTM: Cell detection by soft instance segmentation and consideration of cell-tissue interaction

https://github.com/lhy1007/m3c2

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Joint Modelling Histology and Molecular Markers for Cancer Classification

https://github.com/lingxitong/CAMELYON-PLUS-BENCHMARK

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Towards a Comprehensive Benchmark for Pathological Lymph Node Metastasis in Breast Cancer Sections

https://github.com/litaoyang-jet/tpmil

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TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification

https://github.com/liupei101/DSCA

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DSCA: A Dual-Stream Network with Cross-Attention on Whole-Slide Image Pyramids for Cancer Prognosis

https://github.com/liupei101/advmil

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AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images

https://github.com/liupei101/vlsa

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Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology

https://github.com/lmxmercy/mgct

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MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

https://github.com/lotterlab/fluoroformer

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Fluoroformer: Scaling multiple instance learning to multiplexed images via attention-based channel fusion

https://github.com/lts5/distill-soda

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Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

https://github.com/lts5/proto-sf-osda

공식 코드

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

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