Latest AI and machine learning research in medical education for healthcare professionals.
Fine-grained image retrieval via hand-drawn sketches or textual descriptions remains a critical challenge due to inherent modality gaps. While hand-drawn sketches capture complex structural contours, they lack color and texture, which text effectively provides despite omitting spatial contours. Motivated by the complementary nature of these modalities, we propose the Sketch and Text Based Image Re...
Segmentation is a critical task in computational pathology, as it identifies areas affected by disease or abnormal growth and is essential for diagnosis and treatment. However, acquiring high-quality pixel-level supervised segmentation data requires significant workload demands from experienced pathologists, limiting the application of deep learning. To overcome this challenge, relaxing the label ...
We present Nucleus-Image, a text-to-image generation model that establishes a new Pareto frontier in quality-versus-efficiency by matching or exceedin...
Letters of recommendation (LoRs) can carry patterns of implicitly gendered language that can inadvertently influence downstream decisions, e.g. in hir...
Background: The urgent care departments in Europe face a structural paradox: accelerating digitalisation is accompanied by a patient population that i...
Accurate assessment of spheno-occipital synchondrosis (SOS) maturation is a key indicator of craniofacial growth and a critical determinant for orthod...
The integration of Large Language Models (LLMs) into clinical decision support is critically obstructed by their opaque and often unreliable reasoning...
Fine-tuning pretrained image classifiers is standard practice, yet which individual samples are forgotten during this process, and whether forgetting ...
Flow-based image generative models exhibit stable training and produce high quality samples when using multi-step sampling procedures. One-step genera...
Virtual Try-On (VTON) has seen rapid advancements, providing a strong foundation for generative fashion tasks. However, the inverse problem, Virtual T...
Extending LLM context windows is hindered by scarce high-quality long-context data. Recent methods synthesize data with genuine long-range dependencie...
Diffusion-based image generation models have advanced rapidly but pose a safety risk due to their potential to generate Not-Safe-For-Work (NSFW) conte...
The advent of agentic multimodal models has empowered systems to actively interact with external environments. However, current agents suffer from a p...
A key inefficiency in diffusion training occurs when a randomly initialized network, lacking visual priors, encounters gradients from the full complex...
Developing optical systems for free-space applications requires simulation tools that accurately capture turbulence-induced wavefront distortions and ...
Large language models (LLMs) have demonstrated strong capabilities in medical question answering; however, purely parametric models often suffer from ...
Automated recognition of autistic behaviors in children is essential for early intervention and objective clinical assessment. However, the developmen...
We introduce a nonnegative functional on the space of line arrangements in $\mathbb{P}^2$ that vanishes precisely on free arrangements, obtained as a ...
Video frame interpolation aims to synthesize realistic intermediate frames between given endpoints while adhering to specific motion semantics. While ...
Existing visual grounding benchmarks primarily evaluate alignment between image regions and literal referring expressions, where models can often succ...