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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/4m4n5/saasn-stain-normalization

공식 코드

Self-Attentive Adversarial Stain Normalization

https://github.com/AICAN-Research/FAST-Pathology

공식 코드

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

https://github.com/AIGeeksGroup/PathoHR

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PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images

https://github.com/AIMLab-UBC/MIDL2020

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Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer Learning

https://github.com/AdalbertoCq/Adversarial-learning-of-cancer-tissue-representations

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Adversarial learning of cancer tissue representations

https://github.com/AlexeyAB/darknet

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An Investigation into Glomeruli Detection in Kidney H&E and PAS Images using YOLO

https://github.com/BMIRDS/deepslide

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Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

https://github.com/Calvin-Pang/MAg

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MAg: a simple learning-based patient-level aggregation method for detecting microsatellite instability from whole-slide images

https://github.com/ChristianMarzahl/EIPH_WSI

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Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides

https://github.com/ChristianMarzahl/Exact

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EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version control

https://github.com/DIAGNijmegen/pathology-streaming-pipeline

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Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels

https://github.com/DIDSR/HTT

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A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study

https://github.com/DeepMIALab/AI-FFPE

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Deep Learning-based Frozen Section to FFPE Translation

https://github.com/DeepPathology/CCMCTBiMultinucleated

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Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors

https://github.com/DeepPathology/CCMCT_Bi_Multinucleated

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Dataset on Bi- and Multi-Nucleated Tumor Cells in Canine Cutaneous Mast Cell Tumors

https://github.com/DeepPathology/MITOS_WSI_CMC

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A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research

https://github.com/DeepPathology/SlideRunner

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SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images

https://github.com/Dylan-H-Wang/msf-wsi

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

https://github.com/EIDOSlab/UNITOPATHO

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UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading

https://github.com/FraunhoferMEVIS/MedicalMultitaskModeling

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Tissue Concepts: supervised foundation models in computational pathology

https://github.com/HKU-MedAI/ConcepPath

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Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis

https://github.com/IrisDigitalPathology/Iris-File-Extension

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The Iris File Extension

https://github.com/JJ-ZHOU-Code/RobustMultiModel

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Robust Multimodal Survival Prediction with the Latent Differentiation Conditional Variational AutoEncoder

https://github.com/JiuyangDong/HDMIL

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Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learning

https://github.com/KatherLab/COBRA

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Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning

https://github.com/KatherLab/STAMP

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From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology

https://github.com/KrishnaswamyLab/DiffKillR

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DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images

https://github.com/LG-AI-EXAONE/EXAONEPath

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EXAONEPath 1.0 Patch-level Foundation Model for Pathology

https://github.com/MEDAL-IITB/Fast_WSI_Color_Norm

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Fast GPU-Enabled Color Normalization for Digital Pathology

https://github.com/MIDA-group/Cell-Detection

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A Comparison of Deep Learning Methods for Cell Detection in Digital Cytology

https://github.com/MIDA-group/OralScreen

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A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

https://github.com/MahsaPaknezhad/WSIRegistration

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Regional Registration of Whole Slide Image Stacks Containing Highly Deformed Artefacts

https://github.com/MarvinLer/tcga_segmentation

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Weakly supervised multiple instance learning histopathological tumor segmentation

https://github.com/NeelKanwal/Vision-Transformers-for-Small-Histological-Datasets-Learned-Through-Knowledge-Distillation

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Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation

https://github.com/Paige-AI/paige-ml-sdk

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Virchow: A Million-Slide Digital Pathology Foundation Model

https://github.com/RogerAmundsen/Melanoma-Diagnosis-and-Localization-from-Whole-Slide-Images-using-Convolutional-Neural-Networks

공식 코드

Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images

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