6 accepted papers
Deep learning models for medical image analysis often act as "black boxes," seldom aligning with clinical guidelines or explicitly linking decisions to supporting evidence. This is especially critical in Alzheimer's disease (AD), where predictions should be grounded in both anatomical and clinical f
Brain MRI segmentation is critical for diagnosis and treatment planning, but existing methods are often limited by their task-specific designs and lack of generalizability. A significant challenge lies in integrating multiple brain imaging datasets with varying structural labels for comprehensive an…
Accurate alignment of medical images is essential for effective treatment evaluation and disease monitoring. However, many existing image registration methods are designed for healthy images and face challenges when applied to pathological images, such as those containing tumors. Furthermore, method…
Understanding medical image volumes is crucial in healthcare, yet most current models for classification and segmentation often focus narrowly on task-specific features without capturing the broader medical context. To address this, we introduce Med3DInsight, a pre-training framework that enhances 3…
Learning from observation (LfO) offers a new paradigm for transferring task behavior to robots. LfO requires the robot to observe the task being performed and decompose the sensed streaming data into sequences of state-action pairs, which are then input to LfO methods. Thus, recognizing the state-ac…
Trends in longitudinal or cross-sectional studies over time are often captured through regression models. In their simplest manifestation, these regression models are formulated in $R^n$. However, in the context of imaging studies, the objects of interest which are to be regressed are frequently bes…