Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images
This work addresses the problem of improving speed and accuracy in cancer diagnosis for clinical practice, though it is incremental as it applies existing deep-learning techniques to a specific medical imaging task.
The authors tackled automated region of interest detection in melanocytic skin tumor whole-slide images using a patch-based deep-learning method, achieving 93.94% accuracy in slide classification and 41.27% intersection over union in detection on a test set of five TCGA-SKCM slides.
Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology help us to reduce costs and increase the speed and accuracy of regions of interest detection and cancer diagnosis. In this work, we propose a patch-based region of interest detection method for melanocytic skin tumor whole-slide images. We work with a dataset that contains 165 primary melanomas and nevi Hematoxylin and Eosin whole-slide images and build a deep-learning method. The proposed method performs well on a hold-out test data set including five TCGA-SKCM slides (accuracy of 93.94\% in slide classification task and intersection over union rate of 41.27\% in the region of interest detection task), showing the outstanding performance of our model on melanocytic skin tumor. Even though we test the experiments on the skin tumor dataset, our work could also be extended to other medical image detection problems, such as various tumors' classification and prediction, to help and benefit the clinical evaluation and diagnosis of different tumors.