Recognizing Basal Cell Carcinoma on Smartphone-Captured Digital Histopathology Images with Deep Neural Network.

Researchers

Journal

Modalities

Models

Abstract

Pioneering effort has been made to facilitate the pathology recognition in malignancies based on whole slide images(WSI) through deep learning approaches. It remains unclear whether we can accurately detect and locate BCC using smartphone-captured images.
To develop deep neural network frameworks for accurate BCC recognition and segmentation based on smartphone-captured microscopic ocular images (MOI).
We collected a total of 8046 MOIs, 6610 of which had binary classification labels and the other 1436 had pixel-wise annotations. Meanwhile, 128 WSIs were collected for comparison. Two deep learning frameworks were created. “Cascade” framework had a classification model for identifying hard cases (images with low prediction confidence), and a segmentation model for further in-depth analysis of the hard cases. “Segmentation” framework directly segmented and classified all images. Sensitivity, specificity, and AUC were used to evaluate the overall performance of BCC recognition.
The MOI- and WSI-based models achieved comparable AUCs around 0·95. The “cascade” framework achieved 0·93 sensitivity and 0·91 specificity. The “segmentation” framework was more accurate but required more computational resources, achieving 0·97 sensitivity, 0·94 specificity, and 0·987 AUC. The runtime of the “segmentation” framework was 15·3 ± 3·9second (s) per image, whereas the “cascade” framework was 4·1 ± 1·4s. Additionally the “segmentation” framework achieved 0·863 mean intersection over union (mIoU).
Based on the accessible microscopic ocular images via smartphone photography, we developed two deep learning frameworks for recognizing BCC pathologically with high sensitivity and specificity. This work opens a new avenue for automatic BCC diagnosis in different clinic scenarios. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.