Patchnet: Interpretable Neural Networks for Image Classification

Abstract

Understanding how a complex machine learning model makes a classification decision is essential for its acceptance in sensitive areas such as health care. Towards this end, we present PatchNet, a method that provides the features indicative of each class in an image using a tradeoff between restricting global image context and classification error. We mathematically analyze this tradeoff, demonstrate Patchnet's ability to construct sharp visual heatmap representations of the learned features, and quantitatively compare these features with features selected by domain experts by applying PatchNet to the classification of benign/malignant skin lesions from the ISBI-ISIC 2017 melanoma classification challenge.

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216

References in corpus (5)

• The NumPy array: a structure for efficient numerical computation
• Striving for Simplicity: The All Convolutional Net
• Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
• Image Classification of Melanoma, Nevus and Seborrheic Keratosis by Deep Neural Network Ensemble
• RECOD Titans at ISIC Challenge 2017

Cited by in corpus (1)

• Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis