Latest AI and machine learning research in medicare for healthcare professionals.
Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventional settings, such as perturbation experiments in genomics, exchangeability often holds only within subsets of interventions that leave a target variable "unaffected" (e.g., non-descendants of an intervened node in a ca...
3D Gaussian Splatting (3DGS) has recently emerged as a promising approach in novel view synthesis, combining photorealistic rendering with real-time efficiency. However, its success heavily relies on dense camera coverage; under sparse-view conditions, insufficient supervision leads to irregular Gaussian distributions, characterized by globally sparse coverage, blurred background, and distorted hi...
Clinical deployment of foundation models requires decision policies that operate under explicit error budgets, such as a cap on false-positive clinica...
Cancer is often driven by specific combinations of an estimated two to nine gene mutations, known as multi-hit combinations. Identifying these combina...
Weakly supervised object localization (WSOL) aims to localize target objects in images using only image-level labels. Despite recent progress, many ap...
Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiri...
CLIP models learn transferable multi-modal features via image-text contrastive learning on internet-scale data. They are widely used in zero-shot clas...
Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach by prov...
High genomic variability among viral species makes sequence classification highly dependent on multiple sequence alignment (MSA) methods, which are bo...
Single-image 3D generation with part-level structure remains challenging: learned priors struggle to cover the long tail of part geometries and mainta...
Medical vision-language models (VLMs) are strong zero-shot recognizers for medical imaging, but their reliability under domain shift hinges on calibra...
Face morphing attacks are widely recognized as one of the most challenging threats to face recognition systems used in electronic identity documents. ...
Background and Aims: Alcohol use disorder (AUD) remains a major public health concern, with persistent disparities in access to evidence-based treatme...
We propose a novel method for establishing correspondence between two sequences of 2D images. One particular application of this technique is slice-le...
We present MedXIAOHE, a medical vision-language foundation model designed to advance general-purpose medical understanding and reasoning in real-world...
Video Language Models (VideoLMs) empower AI systems to understand temporal dynamics in videos. To fit to the maximum context window constraint, curren...
Fanconi anemia (FA) is a rare genetic disorder of impaired DNA repair characterized by progressive bone marrow failure, congenital malformations, and ...
Existing multimodal retrieval systems excel at semantic matching but implicitly assume that query-image relevance can be measured in isolation. This p...
Accurate prediction of outcomes is crucial for clinical decision-making and personalized patient care. Supervised machine learning algorithms, which a...
We present a principled framework for confidence estimation in computed tomography (CT) reconstruction. Based on the sequential likelihood mixing fram...