arXiv:2109.00816
Deep Learning-based mitosis detection in breast cancer histologic samples
원문 초록
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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Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation
Mitosis Detection from Partial Annotation by Dataset Generation via Frame-Order Flipping
A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch
Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer
ReCasNet: Improving consistency within the two-stage mitosis detection framework
Assessing domain adaptation techniques for mitosis detection in multi-scanner breast cancer histopathology images
토론
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