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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/nki-ai/dlup-lightning-mil

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DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancer

https://github.com/nki-ai/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/nklskrmnn/hiegnet

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HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification

https://github.com/nmduonggg/fddm

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Bridging Classification and Segmentation in Osteosarcoma Assessment via Foundation and Discrete Diffusion Models

https://github.com/nvtien457/nearbypatchcl

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NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide Images

https://github.com/nyumedml/conditional_ssl_hist

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Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learning

https://github.com/ogundipe-l/edcnn

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Deep Neural Networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer

https://github.com/olgarithmics/ICLR_CAMIL

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CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images

https://github.com/panliangrui/acm_mm_2025

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DLiPath: A Benchmark for the Comprehensive Assessment of Donor Liver Based on Histopathological Image Dataset

https://github.com/panliangrui/nips2025

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PathGene: Benchmarking Driver Gene Mutations and Exon Prediction Using Multicenter Lung Cancer Histopathology Image Dataset

https://github.com/persam-mil/persam

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Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records

https://github.com/phiwei/prostate_coexpression

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Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks

https://github.com/pkeller00/histokernel

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HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling

https://github.com/pnu-amilab/cleric

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Learned Image Compression and Restoration for Digital Pathology

https://github.com/puzzle-ai/unpuzzle

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UnPuzzle: A Unified Framework for Pathology Image Analysis

https://github.com/quiil/falformer

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FALFormer: Feature-aware Landmarks self-attention for Whole-slide Image Classification

https://github.com/r-j96/stainfuser

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StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images

https://github.com/rajpurkarlab/lymphoml

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LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype

https://github.com/raktim-mondol/graphite

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GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology

https://github.com/raktim-mondol/hist2rna

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hist2RNA: An efficient deep learning architecture to predict gene expression from breast cancer histopathology images

https://github.com/ransulab/eocprognosis

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EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images

https://github.com/rantalainengroup/weep

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WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology

https://github.com/rawansaifaldeen/her2-scoring_mil-attention-model

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Leveraging Transfer Learning and Multiple Instance Learning for HER2 Automatic Scoring of H\&E Whole Slide Images

https://github.com/rina-ding/gat-mamba

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Combining Graph Neural Network and Mamba to Capture Local and Global Tissue Spatial Relationships in Whole Slide Images

https://github.com/rtlucassen/melanocytic_lesion_triaging

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Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions

https://github.com/rtlucassen/slidesegmenter

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Tissue Cross-Section and Pen Marking Segmentation in Whole Slide Images

https://github.com/rustins/crossfusion

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CrossFusion: A Multi-Scale Cross-Attention Convolutional Fusion Model for Cancer Survival Prediction

https://github.com/sacmehta/WSISegmentation

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Learning to Segment Breast Biopsy Whole Slide Images

https://github.com/scechnicka/URCDM

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URCDM: Ultra-Resolution Image Synthesis in Histopathology

https://github.com/scjjb/MultiscalePathGraph

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Multi-Resolution Histopathology Patch Graphs for Ovarian Cancer Subtyping

https://github.com/scjjb/dras-mil

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Efficient subtyping of ovarian cancer histopathology whole slide images using active sampling in multiple instance learning

https://github.com/scjjb/hipt_abmil_atec23

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Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning

https://github.com/scjjb/midog_domain_adaptation

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Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images

https://github.com/scjjb/ovarian_features

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A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classification

https://github.com/sharibox/histopathology-inkRemoval

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Ink removal from histopathology whole slide images by combining classification, detection and image generation models

https://github.com/sharonpeled/psa-mil

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PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classification

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