Confidence Aware Neural Networks for Skin Cancer Detection
This work addresses the need for uncertainty quantification in medical imaging to build clinician trust, though it is incremental as it applies existing uncertainty methods to a specific domain.
The paper tackled the problem of deep neural networks lacking confidence estimates in skin cancer detection from images, presenting three methods for quantifying uncertainties and showing that ensemble approaches are more reliable in flagging risky predictions with high uncertainty.
Deep learning (DL) models have received particular attention in medical imaging due to their promising pattern recognition capabilities. However, Deep Neural Networks (DNNs) require a huge amount of data, and because of the lack of sufficient data in this field, transfer learning can be a great solution. DNNs used for disease diagnosis meticulously concentrate on improving the accuracy of predictions without providing a figure about their confidence of predictions. Knowing how much a DNN model is confident in a computer-aided diagnosis model is necessary for gaining clinicians' confidence and trust in DL-based solutions. To address this issue, this work presents three different methods for quantifying uncertainties for skin cancer detection from images. It also comprehensively evaluates and compares performance of these DNNs using novel uncertainty-related metrics. The obtained results reveal that the predictive uncertainty estimation methods are capable of flagging risky and erroneous predictions with a high uncertainty estimate. We also demonstrate that ensemble approaches are more reliable in capturing uncertainties through inference.