Area under the ROC curve, aka AUC, is a measure of choice for assessing the performance
of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that …

Deep learning (DL) approaches to medical image analysis tasks have recently become
popular; however, they suffer from a lack of human interpretability critical for both increasing …

Prototype-based methods use interpretable representations to address the black-box nature
of deep learning models, in contrast to post-hoc explanation methods that only approximate …

Explaining the inner workings of deep neural network models have received considerable
attention in recent years. Researchers have attempted to provide human parseable …

Algorithmic approaches to interpreting machine learning models have proliferated in recent
years. We carry out human subject tests that are the first of their kind to isolate the effect of …

Automated diagnosis using deep neural networks in chest radiography can help radiologists
detect life-threatening diseases. However, existing methods only provide predictions without …

Existing prototypical-based models address the black-box nature of deep learning.
However, they are sub-optimal as they often assume separate prototypes for each class …

In this work, we introduce an extension to ProtoPNet called ProtoPShare which shares
prototypical parts between classes. To obtain prototype sharing we prune prototypical parts …

Influence functions approximate the" influences" of training data-points for test predictions
and have a wide variety of applications. Despite the popularity, their computational cost …

Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …