Deep Learning-based mitosis detection in breast cancer histologic samples

2 Sep 2021 · Michel Halmes, Hippolyte Heuberger, Sylvain Berlemont ·

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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Mitosis Detection

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1x1 Convolution • Average Pooling • Batch Normalization • Concatenated Skip Connection • Convolution • Dense Block • Dense Connections • DenseNet • Dropout • Global Average Pooling • Kaiming Initialization • Max Pooling • ReLU • Softmax

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Deep Learning-based mitosis detection in breast cancer histologic samples

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