SkiNet: A Deep Learning Solution for Skin Lesion Diagnosis with Uncertainty Estimation and Explainability

Abstract

Skin cancer is considered to be the most common human malignancy. Around 5 million new cases of skin cancer are recorded in the United States annually. Early identification and evaluation of skin lesions is of great clinical significance, but the disproportionate dermatologist-patient ratio poses significant problem in most developing nations. Therefore a deep learning based architecture, known as SkiNet, is proposed with an objective to provide faster screening solution and assistance to newly trained physicians in the clinical diagnosis process. The main motive behind Skinet's design and development is to provide a white box solution, addressing a critical problem of trust and interpretability which is crucial for the wider adoption of Computer-aided diagnosis systems by the medical practitioners. SkiNet is a two-stage pipeline wherein the lesion segmentation is followed by the lesion classification. In our SkiNet methodology, Monte Carlo dropout and test time augmentation techniques have been employed to estimate epistemic and aleatoric uncertainty, while saliency-based methods are explored to provide post-hoc explanations of the deep learning models. The publicly available dataset, ISIC-2018, is used to perform experimentation and ablation studies. The results establish the robustness of the model on the traditional benchmarks while addressing the black-box nature of such models to alleviate the skepticism of medical practitioners by incorporating transparency and confidence to the model's prediction.

References in corpus (9)

• Axiomatic Attribution for Deep Networks
• MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
• Weight Uncertainty in Neural Networks
• Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
• Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks
• Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
• Uncertainty and Interpretability in Convolutional Neural Networks for Semantic Segmentation of Colorectal Polyps
• Robust Tumor Localization with Pyramid Grad-CAM
• Understanding Behavior of Clinical Models under Domain Shifts