Deep residual Hough voting for mitotic cell detection in histopathology images

Cell detection in microscopy images is a common and challenging task. We propose a new approach for mitotic cell detection in histopathology images, which is based on a Deep Residual Network architecture combined with Hough voting. We propose a voting layer for neural networks and introduce a novel loss function. Our approach is learned from scratch using cell centroids and the original images. We benchmarked our approach on the challenging AMIDA13 dataset containing histology images of invasive breast carcinoma. It turned out that our approach achieved competitive results.

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