Deep Learning-based mitosis detection in breast cancer histologic samples

09/02/2021

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This is the submission for mitosis detection in the context of the MIDOG 2021 challenge. It is based on the two-stage objection model Faster RCNN as well as DenseNet as a backbone for the neural network architecture. It achieves a F1-score of 0.6645 on the Preliminary Test Phase Leaderboard.

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Deep Learning-based mitosis detection in breast cancer histologic samples

Cascade RCNN for MIDOG Challenge

Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer

Mitosis Detection Under Limited Annotation: A Joint Learning Approach

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

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology

Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge

Deep Learning-based mitosis detection in breast cancer histologic samples

Related Research

Cascade RCNN for MIDOG Challenge

Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

A Robust and Effective Approach Towards Accurate Metastasis Detection and pN-stage Classification in Breast Cancer

Mitosis Detection Under Limited Annotation: A Joint Learning Approach

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

A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology

Detecting Mitosis against Domain Shift using a Fused Detector and Deep Ensemble Classification Model for MIDOG Challenge