Latest AI and machine learning research in work force for healthcare professionals.
Composed Image Retrieval (CIR) uses a reference image plus a natural-language edit to retrieve images that apply the requested change while preserving other relevant visual content. Classic fusion pipelines typically rely on supervised triplets and can lose fine-grained cues, while recent zero-shot approaches often caption the reference image and merge the caption with the edit, which may miss imp...
Foundation models for medical imaging are typically pretrained on increasingly large datasets, following a "scale-at-all-costs" paradigm. However, this strategy faces two critical challenges: large-scale medical datasets often contain substantial redundancy and severe class imbalance that bias representation learning toward over-represented patterns, and indiscriminate training regardless of heter...
Colonoscopy video generation delivers dynamic, information-rich data critical for diagnosing intestinal diseases, particularly in data-scarce scenario...
In recent times, large datasets hinder efficient model training while also containing redundant concepts. Dataset distillation aims to synthesize comp...
Background: Objective Structured Clinical Examination (OSCE; Clinical Performance Examination [CPX] in South Korea) is a high-stakes assessment of cli...
Text-to-image (T2I) models are rapidly gaining popularity, yet their outputs often lack geographical diversity, reinforce stereotypes, and misrepresen...
Large Language Models (LLMs) are converging towards a singular Artificial Hivemind, where shared Nature (pre-training priors) result in a profound col...
Background: Diagnostic errors are a leading cause of preventable patient harm, often occurring during early clinical encounters where diagnostic uncer...
Background: Typing in the electronic health record (EHR) takes up healthcare providers' time and cognitive space and constitutes a substantial adminis...
Monocular 3D object detection (M3OD) is intrinsically ill-posed, hence training a high-performance deep learning based M3OD model requires a humongous...
Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following. But do they perform well as s...
Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following. But do they perform well as s...
Background: EHR documentation and chart review contribute to clinician workload and burnout. To alleviate pre-charting burden, Epic has released a new...
Despite high semantic alignment, modern text-to-image (T2I) generative models still struggle to synthesize diverse images from a given prompt. This la...
Compositional generalization, the ability to reason about novel combinations of familiar concepts, is fundamental to human cognition and a critical ch...
Sodium azide mutagenesis offers a powerful approach to generate genetic diversity for rice improvement, yet comprehensive characterization of mutant p...
Ecological theory predicts that local species richness can influence biogeographic rates of speciation, extinction, and dispersal. For instance, incre...
Dataset distillation compresses the original data into compact synthetic datasets, reducing training time and storage while retaining model performanc...
Text-to-video generation is expensive, so only a few samples are typically produced per prompt. In this low-sample regime, maximizing the value of eac...
This study investigates the impact of regularization of latent spaces through truncation on the quality of generated test inputs for deep learning cla...