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Deep Convolutional Neural Network-Based Mitosis Detection in Invasive Carcinoma of Breast by Smartphone-Based Histologic Image Acquisition

Seokhwi Kim et al. — USCAP 2016

Mitosis counting is time and labor-consuming work and it frequently reveals inter-observer variability. Although deep convolutional neural network, the most accurate image classification algorithm, has been used for detecting mitosis, only public data sets were tested and it had never been utilized for routine histologic slide images. Recently, smartphone cameras with adaptors to the microscope were tried for easier image acquisition and they significantly resolved a barrier for applying computer algorithms to analyze histologic images. Histologic slides of 70 invasive ductal carcinomas of breast were selected and 1761 high-power field histologic images (400x) were acquired by using smartphone application with an adaptor attached to the microscope manufactured by us. Mitoses were annotated by four pathologists blindly. More than three pathologists’ concordance was regarded as true. 2004 mitotic cells and 801600 non-mitotic cells from 60 cases were divided into 10 sets and the algorithm was sequentially trained using fine-tuning method. After the training, ten patients’ images were tested for the concordance of detection with pathologists. During the algorithm training, sensitivity for mitosis detection was calculated between 75-83%. Specificity for mitosis detection was increased to achieve 97% as we trained the algorithm with more images. The trained algorithm identified 189 mitoses in 748 images from 10 cases and showed 79% sensitivity and 96% specificity for detecting mitosis compared to the pathologists. The detected mitoses were displayed in the application within 14 seconds in average. The proposed deep convolutional neural network-based mitosis detection system revealed remarkable sensitivity and specificity, and the performance improved as more images were utilized for training. Along with the smartphone application and the adaptor we manufactured, it assists pathologists to identify mitosis so that reduce time and labor costs, while resulting objective diagnosis.

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Seokhwi Kim1, Jungin Lee2, Soo-hyun Hwang1, Sooyoun Cho1, Sangheum Hwang2, Hyo-Eun Kim2, Hyemin Shim2, Miso Yang3 and Sangyong Song1

1Departments of Pathology and Translational Medicine, Samsung Medical Center, 2Lunit Inc., 3College of Fine Arts, Seoul National University

USCAP 2016

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