Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Accurate medical image segmentation requires effective modeling of both global anatomical structures and fine-grained boundary details. Recent state space models (e.g., Vision Mamba) offer efficient long-range dependency modeling. However, their one-dimensional serialization weakens local spatial continuity and high-frequency representation. To this end, we propose SpectralMamba-UNet, a novel freq...
High stress occurs when 3D heterogeneous IC packages are subjected to thermal cycling at extreme temperatures. Stress mainly occurs at the interface between different materials. We investigate stress image using latent space representation which is based on using deep generative model (DGM). However, most DGM approaches are unsupervised, meaning they resort to image pairing (input and output) to t...
The rapid deployment of deep neural network (DNN) accelerators in safety-critical domains such as autonomous vehicles, healthcare systems, and financi...
Deep learning has significantly advanced automated brain tumor diagnosis, yet clinical adoption remains limited by interpretability and computational ...
Graph-based medical image segmentation represents anatomical structures using boundary graphs, providing fixed-topology landmarks and inherent populat...
We propose a template-driven triangulation framework that embeds raster- or segmentation-derived boundaries into a regular triangular grid for stable ...
Across minimal neural networks and small transformer models, we demonstrate that experience ordering alone can produce durable, irreversible behaviora...
Background: Australian health practitioners are regulated under the Health Practitioner Regulation National Law, with serious conduct matters referred...
Image-based Virtual Try-On (VTON) concerns the synthesis of realistic person imagery through garment re-rendering under human pose and body constraint...
Unsupervised image segmentation is a critical task in computer vision. It enables dense scene understanding without human annotations, which is especi...
Green's functions characterize the fundamental solutions of partial differential equations; they are essential for tasks ranging from shape analysis t...
Semantic Change Detection (SCD) aims to detect and categorize land-cover changes from bi-temporal remote sensing images. Existing methods often suffer...
In critical decision support systems based on medical imaging, the reliability of AI-assisted decision-making is as relevant as predictive accuracy. A...
Boundary detection of irregular and translucent objects is an important problem with applications in medical imaging, environmental monitoring and man...
Accurate segmentation of MRI brain images is essential for image analysis, diagnosis of neuro-logical disorders and medical image computing. In the de...
Deep search agents, which autonomously iterate through multi-turn web-based reasoning, represent a promising paradigm for complex information-seeking ...
Score-based diffusion models are a recently developed framework for posterior sampling in Bayesian inverse problems with a state-of-the-art performanc...
IMU-based Human Activity Recognition (HAR) has enabled a wide range of ubiquitous computing applications, yet its dominant clip classification paradig...
Background: Generating synthetic data using artificial intelligence, such as large language models (LLMs), is a useful strategy in public health becau...
Background: Depression is biologically heterogeneous, and first-episode depression (FED) carries a high risk of recurrence that is poorly captured by ...