Understanding the severity of conditions shown in images in medical diagnosis is crucial
serving as a key guide for clinical assessment treatment as well as evaluating longitudinal …

Recently, contrastive learning has received significant attention in various classification
tasks of natural images. However, current contrastive learning frameworks display …

Representation learning offers a conduit to elucidate distinctive features within the latent
space and interpret the deep models. However, the randomness of lesion distribution and …

Deep learning models offer unprecedented opportunities for diagnosis, prognosis, and
treatment planning. However, conventional deep learning pipelines often encounter …

Medical image classification has been widely adopted in medical image analysis. However,
due to the difficulty of collecting and labeling data in the medical area, medical image …

The use of machine learning models to predict clinical outcomes from (longitudinal)
electronic health record (EHR) data is becoming increasingly popular due to advances in …

Understanding of spatial attributes is central to effective 3D radiology image analysis where
crop-based learning is the de facto standard. Given an image patch, its core spatial …

Medical image datasets are often imbalanced due to biases in data collection and limitations
in acquiring data for rare conditions. Addressing class imbalance is crucial for developing …

Deep learning models have gained remarkable performance on a variety of image
classification tasks. However, many models suffer from limited performance in clinical or …

Contrastive Analysis is a sub-field of Representation Learning that aims at separating
common factors of variation between two datasets, a background (ie, healthy subjects) and a …