https://github.com/DeepPathology/SlideRunner
공식 코드SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images
Papers with Code 아카이브
Mitosis Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MEDAL-IITB/MIDOG
공식 코드Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer
https://github.com/antifen/mitosis-nuclei-detection
공식 코드A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
https://github.com/cmb-chula/recasnet
공식 코드ReCasNet: Improving consistency within the two-stage mitosis detection framework
https://github.com/mikevoets/jama16-retina-replication
공식 코드https://github.com/naivete5656/MDMLM
공식 코드https://github.com/naivete5656/mdpafof
공식 코드Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping
https://github.com/onehour0108/mitdet
공식 코드Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
https://github.com/scjjb/midog_domain_adaptation
공식 코드https://github.com/tueimage/se2cnn
공식 코드Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis
https://github.com/2023-MindSpore-4/Code8/tree/main/3dcnn
https://github.com/CODAIT/deep-histopath
A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology
https://github.com/MindSpore-paper-code-2/code399/tree/main/3dcnn
https://github.com/deepmicroscopy/midog
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
https://github.com/deeppathology/midog
Quantifying the Scanner-Induced Domain Gap in Mitosis Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.