Urology

STARS: Shared-specific Translation and Alignment for missing-modality Remote Sensing Semantic Segmentation

Multimodal remote sensing technology significantly enhances the understanding of surface semantics by integrating heterogeneous data such as optical images, Synthetic Aperture Radar (SAR), and Digital Surface Models (DSM). However, in practical applications, the missing of modality data (e.g., optical or DSM) is a common and severe challenge, which leads to performance decline in traditional multi...

Understanding the Transfer Limits of Vision Foundation Models

Foundation models leverage large-scale pretraining to capture extensive knowledge, demonstrating generalization in a wide range of language tasks. By comparison, vision foundation models (VFMs) often exhibit uneven improvements across downstream tasks, despite substantial computational investment. We postulate that this limitation arises from a mismatch between pretraining objectives and the deman...

Transfer Learning from One Cancer to Another via Deep Learning Domain Adaptation

Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer...

Medea: An omics AI agent for therapeutic discovery

AI agents promise to empower biomedical discovery, but realizing this promise requires the ability to complete transparent, long-horizon analyses usin...

ComPRePS: Unlocking Scalable AI Analysis for Computational Renal Pathology

Digital pathology using whole slide imaging (WSI) and artificial intelligence (AI) has the potential to transform diagnostic workflows, but adoption r...

Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the u...

Aligning Artificial Intelligence Prediction Targets with Clinical Workflows Using Human Centered Design Methods

Artificial intelligence models in healthcare often fail to improve patient outcomes despite strong predictive performance because they are frequently ...

Mass spectrometry and machine learning for classification and molecular phenotyping of renal cell carcinoma and benign tumors

Pathology classification of cancer tumor subtypes and benign tumors can be difficult due to cellular heterogeneity and similar morphological features....

Onco-Seg: Adapting Promptable Concept Segmentation for Multi-Modal Medical Imaging

Medical image segmentation remains a critical bottleneck in clinical workflows, from diagnostic radiology to radiation oncology treatment planning. We...

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states

Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively...

Unifying phylogenetic traversal and deep learning to guide tree exploration

Deep learning offers hope for more efficient phylogenetic inference methods. However, it has yet to have the transformative effect on phylogenetics th...

Prediction of peripheral blood lymphocyte subpopulations after renal transplantation.

Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs)...

Deep learning-based detection of bacterial swarm motion using a single image.

Motility is a fundamental characteristic of bacteria. Distinguishing between swarming and swimming, the two principal forms of bacterial movement, hol...

The accuracy of Machine learning in the prediction and diagnosis of diabetic kidney Disease: A systematic review and Meta-Analysis.

PURPOSE: Machine learning (ML) has gained attention in diabetes management, particularly for predicting and diagnosing diabetic kidney disease (DKD). ...

Accelerating prostate rs-EPI DWI with deep learning: Halving scan time, enhancing image quality, and validating in vivo.

OBJECTIVES: This study aims to evaluate the feasibility and effectiveness of deep learning-based super-resolution techniques to reduce scan time while...

Decoding virulence and resistance in Klebsiella pneumoniae: Pharmacological insights, immunological dynamics, and in silico therapeutic strategies.

Klebsiella pneumoniae (K. pneumoniae) has become a serious global health concern due to its rising virulence and antibiotic resistance. As one of the ...

The impact of clinical history on the predictive performance of machine learning and deep learning models for renal complications of diabetes.

BACKGROUND AND OBJECTIVE: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy. The early identification of i...

Deep learning for early detection of chronic kidney disease stages in diabetes patients: A TabNet approach.

Chronic kidney disease (CKD) poses a significant risk for diabetes patients, often leading to severe complications. Early and accurate CKD stage detec...

A systematic review of AI as a digital twin for prostate cancer care.

Artificial Intelligence (AI) and Digital Twin (DT) technologies are rapidly transforming healthcare, offering the potential for personalized, accurate...

A negative combined effect of exposure to maternal Mn-Cu-Rb-Fe metal mixtures on gestational anemia, and the mediating role of creatinine in the Guangxi Birth Cohort Study (GBCS): Twelve machine learning algorithms.

The link between individual metals and gestational anemia has been established, but the impact of metal mixtures and the mediating role of renal funct...