Abstract Diffusion Probabilistic Models have recently shown remarkable performance in
generative image modeling, attracting significant attention in the computer vision community …

Abstract Whole Slide Images (WSIs) are usually gigapixel in size and lack pixel-level
annotations. The WSI datasets are also imbalanced in categories. These unique …

In real medical data, training samples typically show long-tailed distributions with multiple
labels. Class distribution of the medical data has a long-tailed shape, in which the incidence …

Imaging exams, such as chest radiography, will yield a small set of common findings and a
much larger set of uncommon findings. While a trained radiologist can learn the visual …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

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 …

Federated learning (FL), training deep models from decentralized data without privacy
leakage, has shown great potential in medical image computing recently. However …

Abstract Machine learning holds tremendous promise for transforming the fundamental
practice of scientific discovery by virtue of its data-driven nature. With the ever-increasing …

Chest radiography is a common medical diagnostic procedure, often resulting in a long-
tailed distribution of clinical findings. This challenges standard deep learning methods …

Data imbalance and open-ended distribution are two intrinsic characteristics of the real
visual world. Though encouraging progress has been made in tackling each challenge …