Although computer-aided diagnosis (CAD) is widely used in mammography, conventional
CAD programs that use prompts to indicate potential cancers on the mammograms have not …

The rapid increase in the number of proteins in sequence databases and the diversity of
their functions challenge computational approaches for automated function prediction. Here …

OBJECTIVE. The purpose of this article is to compare traditional versus machine learning–
based computer-aided detection (CAD) platforms in breast imaging with a focus on …

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of
patients at the emergency department is critical to inform decision-making. We propose a …

The key distinguishing property of a Bayesian approach is marginalization instead of
optimization, not the prior, or Bayes rule. Bayesian inference is especially compelling for …

There is a significant need for principled uncertainty reasoning in machine learning systems
as they are increasingly deployed in safety-critical domains. A new approach with …

The Fine-Grained Visual Categorization (FGVC) is challenging because the subtle inter-
class variations are difficult to be captured. One notable research line uses the Global …

Methods based on class activation maps (CAM) provide a simple mechanism to interpret
predictions of convolutional neural networks by using linear combinations of feature maps …

This paper addresses the notable gap in evaluating eXplainable Artificial Intelligence (XAI)
methods for text classification. While existing frameworks focus on assessing XAI in areas …

COVID-19 has cost millions of lives worldwide. The constant mutation of SARS-CoV-2 calls
for thorough research to facilitate the development of variant surveillance. In this work, we …