Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Uncertainty in medical image segmentation is inherently non-uniform, with boundary regions exhibiting substantially higher ambiguity than interior areas. Conventional training treats all pixels equally, leading to unstable optimization during early epochs when predictions are unreliable. We argue that this instability hinders convergence toward Pareto-optimal solutions and propose a region-wise cu...
Significant progress has been made in semi-supervised hyperspectral image (HSI) classification regarding feature extraction and classification performance. However, due to high annotation costs and limited sample availability, semi-supervised learning still faces challenges such as boundary label diffusion and pseudo-label instability. To address these issues, this paper proposes a novel semi-supe...
Range-view projection provides an efficient method for transforming 3D LiDAR point clouds into 2D range image representations, enabling effective proc...
Existing displacement strategies in semi-supervised segmentation only operate on rectangular regions, ignoring anatomical structures and resulting in ...
Unmanned Aerial Vehicle (UAV) applications have become increasingly prevalent in aerial photography and object recognition. However, there are major c...
We introduce NewPINNs, a physics-informing learning framework that couples neural networks with conventional numerical solvers for solving differentia...
The human genome is partitioned at different levels of 3D genome organization, with topologically associating domains (TADs) being among the most well...
Deep learning has substantially advanced medical image segmentation, yet achieving robust generalization across diverse imaging modalities and anatomi...
Image segmentation plays a central role in computer vision. However, widely used evaluation metrics, whether pixel-wise, region-based, or boundary-foc...
Three-dimensional electron microscopy (3D EM) enables the quantitative analysis of cellular ultrastructure. However, large-scale segmentation of whole...
Accurate segmentation of neuronal cells in fluorescence microscopy is a fundamental task for quantitative analysis in computational neuroscience. Howe...
The global impact of COVID-19 has caused a significant rise in the demand for psychological counseling services, creating pressure on existing mental ...
This study aims to develop a novel multi-modal fusion framework for brain tumor segmentation that integrates spatial-language-vision information thr...
Floods are among the most frequent natural hazards and cause significant social and economic damage. Timely, large-scale information on flood extent...
Out-of-Distribution (OoD) segmentation is critical for safety-sensitive applications like autonomous driving. However, existing mask-based methods o...
Out-of-Distribution (OoD) segmentation is critical for safety-sensitive applications like autonomous driving. However, existing mask-based methods o...
Edge detection (ED) remains a fundamental task in computer vision, yet its performance is often hindered by the ambiguous nature of non-edge pixels ...
Building on our previous work introducing Fredholm Neural Networks (Fredholm NNs/ FNNs) for solving integral equations, we extend the framework to t...
Medical AI diagnosis including histopathology segmentation has derived benefits from the recent development of deep learning technology. However, de...
Assisting medical students with clinical reasoning (CR) during clinical scenario training remains a persistent challenge in medical education. This ...