Medical imaging plays a critical role in modern healthcare, enabling
clinicians to accurately diagnose diseases and develop effective treatment
plans. However, noise, often introduced by imaging devices, can degrade image
quality, leading to misint... read more
Mixed modality search -- retrieving information across a heterogeneous corpus
composed of images, texts, and multimodal documents -- is an important yet
underexplored real-world application. In this work, we investigate how
contrastive vision-langu... read more
Understanding the decisions made by deep neural networks is essential in
high-stakes domains such as medical imaging and autonomous driving. Yet, these
models often lack transparency, particularly in computer vision.
Prototypical-parts-based neural... read more
Chest radiography (CXR) plays a crucial role in the diagnosis of various
diseases. However, the inherent class imbalance in the distribution of clinical
findings presents a significant challenge for current self-supervised deep
learning models. The... read more
Automatic classification of Diabetic Retinopathy (DR) can assist
ophthalmologists in devising personalized treatment plans, making it a critical
component of clinical practice. However, imbalanced data distribution in the
dataset becomes a bottlene... read more
Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
Jul 25, 2025
BackgroundNon-contrast computed tomography (NCCT) is the first image for stroke assessment, but its sensitivity for detecting large vessel occlusion (LVO) is limited. Artificial intelligence (AI) algorithms may contribute to a faster LVO diagnosis us... read more
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