Region of Interest Detection in Melanocytic Skin Tumor Whole Slide Images

TL;DR

This paper presents a deep-learning based patch method for detecting regions of interest in whole-slide images of melanocytic skin tumors, achieving high accuracy and promising potential for clinical application.

Contribution

It introduces a novel patch-based deep-learning approach for ROI detection in skin tumor images, demonstrating strong performance on a real dataset.

Findings

Accuracy of 93.94% in slide classification

Intersection over Union rate of 41.27% in ROI detection

Model performance suggests potential for broader medical image analysis

Abstract

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.