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• W3138200812 abstract "The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework MP-MitDet for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier MitosRes-CNN to filter false mitoses. The performance of the proposed MitosRes-CNN is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei." @default.
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• W3138200812 date "2021-03-18" @default.
• W3138200812 modified "2023-10-18" @default.
• W3138200812 title "A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images" @default.
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• W3138200812 doi "https://doi.org/10.1038/s41598-021-85652-1" @default.
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