Large pre-trained transformers have been receiving explosive attention in the past few
years, due to their acculturation for numerous downstream applications via fine-tuning, but …

In the semiconductor manufacturing process, analyzing the defect patterns on a wafer map
is crucial for identifying the causes of the defects. The advent of convolutional neural …

Graphs are omnipresent and GNNs are a powerful family of neural networks for learning
over graphs. Despite their popularity, scaling GNNs either by deepening or widening suffers …

Deep neural networks (DNNs) have rapidly become a de facto choice to medical image
understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image …

AI-powered Medical Imaging has recently achieved enormous attention due to its ability to
provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high …

Chest radiograph reporting is time-consuming, and numerous solutions to automate this
process have been proposed. Due to the complexity of medical information, the variety of …

Recent advancements in retinal vessel segmentation, which employ transformer-based and
domain-adaptive approaches, show promise in addressing the complexity of ocular …

Accurate staging of scars is crucial for the targeted treatment of patients. However, the task is
complicated by the varied development of scars across different individuals and body parts …

Medical image classification is one of the most important tasks for computer-aided
diagnosis. Deep learning models, particularly convolutional neural networks, have been …

Opacities of non-interstitial origin in a pediatric patient's chest radiograph may indicate either
consolidations and/or atelectasis, based on the appropriate clinical context. However, the …