What evidence does deep learning model use to classify Skin Lesions?

Abstract

Melanoma is a type of skin cancer with the most rapidly increasing incidence. Early detection of melanoma using dermoscopy images significantly increases patients' survival rate. However, accurately classifying skin lesions by eye, especially in the early stage of melanoma, is extremely challenging for the dermatologists. Hence, the discovery of reliable biomarkers will be meaningful for melanoma diagnosis. Recent years, the value of deep learning empowered computer-assisted diagnose has been shown in biomedical imaging based decision making. However, much research focuses on improving disease detection accuracy but not exploring the evidence of pathology. In this paper, we propose a method to interpret the deep learning classification findings. Firstly, we propose an accurate neural network architecture to classify skin lesions. Secondly, we utilize a prediction difference analysis method that examines each patch on the image through patch-wised corrupting to detect the biomarkers. Lastly, we validate that our biomarker findings are corresponding to the patterns in the literature. The findings can be significant and useful to guide clinical diagnosis.

5 pages

References in corpus (5)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Axiomatic Attribution for Deep Networks
• Learning Important Features Through Propagating Activation Differences
• Understanding Neural Networks Through Deep Visualization
• Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

Cited by in corpus (4)

• Early Melanoma Diagnosis with Sequential Dermoscopic Images
• On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
• AI-Skin : Skin Disease Recognition based on Self-learning and Wide Data Collection through a Closed Loop Framework
• Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis