Deep neural network or dermatologist?

By Kyle Young, Gareth Booth, Becks Simpson, Reuben Dutton, Sally Shrapnel. 2019

79 citations recorded by the source corpus.

Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify potential failure modes. This is a major barrier to adoption of deep learning in clinical practice. In this paper we ask if two existing local interpretability methods, Grad-CAM and Kernel SHAP, can shed light on convolutional neural networks trained in the context of melanoma detection. Our contributions are (i) we first explore the domain space via a reproducible, end-to-end learning framework that creates a suite of 30 models, all trained on a publicly available data set (HAM10000), (ii) we next explore the reliability of GradCAM and Kernel SHAP in this context via some basic sanity check experiments (iii) finally, we investigate a random selection of models from our suite using GradCAM and Kernel SHAP. We show that despite high accuracy, the models will occasionally assign importance to features that are not relevant to the diagnostic task. We also show that models of similar accuracy will produce different explanations as measured by these methods. This work represents first steps in bridging the gap between model accuracy and interpretability in the domain of skin cancer classification.

Subfields: Artificial Intelligence, Biophysics, Oncology

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Citations in the corpus

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI (2019, neutral): “heatmaps before practical deployment [159].”
Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging (2020, unknown): “trained an Inception neural network model to differentiate benign skin lesions from melanoma on dermoscopy images and examined the performance of saliency maps generated by GradCAM and Kernel-SHAP [18].”
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, methodology): “Interpretable and non-linear models have been successfully applied to many other tasks, including the detection of lesions in diabetic retinopathy data [115], the validation of predictions in dermatology [157], plant stress classiﬁcation [41], or the analysis of galaxy morphologies [164].”
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2021, background): “Zeiler and Fergus (2014) ✓ ✓ ✓ Local Interpretable Model-agnostic Explanations (LIME) 3.”