Heart Transplantation

OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP

We introduce Low-Shot Open-Set Domain Generalization (LSOSDG), a novel paradigm unifying low-shot learning with open-set domain generalization (ODG). While prompt-based methods using models like CLIP have advanced DG, they falter in low-data regimes (e.g., 1-shot) and lack precision in detecting open-set samples with fine-grained semantics related to training classes. To address these challenges...

Is there a future for AI without representation?

This paper investigates the prospects of AI without representation in general, and the proposals of Rodney Brooks in particular. What turns out to be characteristic of Brooks' proposal is the rejection of central control in intelligent agents; his systems has as much or as little representation as traditional AI. The traditional view that representation is necessary for intelligence presupposes ...

Open-Set Plankton Recognition

This paper considers open-set recognition (OSR) of plankton images. Plankton include a diverse range of microscopic aquatic organisms that have an i...

KidneyTalk-open: No-code Deployment of a Private Large Language Model with Medical Documentation-Enhanced Knowledge Database for Kidney Disease

Privacy-preserving medical decision support for kidney disease requires localized deployment of large language models (LLMs) while maintaining clini...

Identifying Risk Factors for Graft Failure due to Chronic Rejection < 15 Years Post-Transplant in Pediatric Kidney Transplants Using Random Forest Machine-Learning Techniques.

BACKGROUND: Chronic rejection forms the leading cause of late graft loss in pediatric kidney transplant recipients. Despite improvement in short-term ...

Hybrid Retrieval for Hallucination Mitigation in Large Language Models: A Comparative Analysis

Large Language Models (LLMs) excel in language comprehension and generation but are prone to hallucinations, producing factually incorrect or unsupp...

EXACT-CT: EXplainable Analysis for Crohn's and Tuberculosis using CT

Crohn's disease and intestinal tuberculosis share many overlapping features such as clinical, radiological, endoscopic, and histological features - ...

Visual Reasoning Evaluation of Grok, Deepseek Janus, Gemini, Qwen, Mistral, and ChatGPT

Traditional evaluations of multimodal large language models (LLMs) have been limited by their focus on single-image reasoning, failing to assess cru...

Rapid Parameter Inference with Uncertainty Quantification for a Radiological Plume Source Identification Problem

In the event of a nuclear accident, or the detonation of a radiological dispersal device, quickly locating the source of the accident or blast is im...

Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data

Large Multimodal Models (LMMs), or Vision-Language Models (VLMs), have shown impressive capabilities in a wide range of visual tasks. However, they ...

Nonlinear Feedback Linearization and LQG/LTR Control: A Comparative Study for a Single-Machine Infinite-Bus System

This paper presents a comparative study of three advanced control strategies for a single-machine infinite-bus (SMIB) system: the nonlinear feedback...

Detection and classification of glomerular lesions in kidney graft biopsies using 2-stage deep learning approach.

Acute allograft rejection in patients undergoing renal transplantation is diagnosed through histopathological analysis of renal graft biopsies, which ...

Leveraging Registers in Vision Transformers for Robust Adaptation

Vision Transformers (ViTs) have shown success across a variety of tasks due to their ability to capture global image representations. Recent studies...

Auto-MedCalc: Automated Biomarkers Discovery and Risk Score Generation with AI Agents

Identifying biomarkers and generating risk scores are usually essential tasks in many biomedicine and clinical scenarios. However, this is a highly hy...

Mapping and reprogramming human tissue microenvironments with MintFlow

Tissue microenvironments reprogram local cellular states in disease, yet current computational spatial methods remain descriptive and do not simulate ...

Machine-Learning-Assisted Exploration of High Entropy-Atom Nanozyme for Anti-Tumor Immunotherapy by Enhancing Enzyme Activity and Disrupting Dual Energy Metabolism

Despite its potential in cancer therapy, single-atom nanozyme (SAzyme) faces challenges like low atomic loading and rapid cancer metabolism. Here, a h...

Clinical and molecular characterisation of primary refractoriness to atezolizumab plus bevacizumab in patients with unresectable hepatocellular carcinoma

Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), characterised by ...

Large vision model framework for automated C. elegans analysis: From static morphometry to dynamic neural activity

Quantitative phenotyping of Caenorhabditis elegans is essential across numerous fields, yet data extraction remains a significant analytical bottlenec...

Machine learning–driven decoding of maternal immune signatures in repeated pregnancy loss

Repeated pregnancy loss (RPL) is a multifactorial condition in which the underlying immunological mechanisms remain incompletely understood. Although ...

Integrated analysis implicates novel insights of NMB into lactate metabolism and immune response prediction in primary glioblastoma

Glioblastoma (GBM), the most aggressive primary brain tumor in adults, exhibits profound treatment resistance and poor prognosis. Despite advances in ...