Urology

Identifying Reasons for ACEI/ARB Non-Use in CKD Using Scalable Clinical NLP with Schema-Guided LLM Augmentation

IMPORTANCE: Although angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) are recommended for people with chronic kidney disease (CKD), they remain underused. Barriers to adherence, such as adverse effects or patient refusal, are frequently embedded within unstructured clinical narratives and are therefore inaccessible to structured data analytics. Scalable nat...

Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale study

Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression remains challenging. This multicenter study developed and validated POCC, a machine learning model for predicting kidney failure risk at 1, 3, and 5 years post-diagnosis in CAKUT patients. Two versions were created using data from 2,249 children. The...

Deep Modeling and Interpretation for Bladder Cancer Classification

Deep models based on vision transformer (ViT) and convolutional neural network (CNN) have demonstrated remarkable performance on natural datasets. How...

Bladder Vessel Segmentation using a Hybrid Attention-Convolution Framework

Urinary bladder cancer surveillance requires tracking tumor sites across repeated interventions, yet the deformable and hollow bladder lacks stable la...

Fast Organ-of-Origin Classification for Digital Pathology Quality Control

Digitizing large histopathology archives requires processing millions of scanned whole slide images that must be validated rapidly. Automated organ-of...

Stroke Lesions as a Rosetta Stone for Language Model Interpretability

Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which model components are truly necessary for language func...

Deep Learning-Enabled Screening of Chronic Kidney Disease from Echocardiography

Chronic kidney disease (CKD) affects nearly 850 million individuals globally; the prevalence of undiagnosed CKD is 60%. Taking advantage of the relati...

Brainstem neurons coordinate the bladder and urethral sphincter for urination

Urination, a vital and conserved process of emptying urine from the urinary bladder in mammals, requires precise coordination between the bladder and ...

Automated Segmentation of Kidney Nephron Structures by Deep Learning Models on Label-free Autofluorescence Microscopy for Spatial Multi-omics Data Acquisition and Mining

Automated spatial segmentation models can enrich spatio-molecular omics analyses by providing a link to relevant biological structures. We developed s...

A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation

Test-Time Adaptation (TTA) offers a practical solution for deploying image segmentation models under domain shift without accessing source data or ret...

Multi-head automated segmentation by incorporating detection head into the contextual layer neural network

Deep learning based auto segmentation is increasingly used in radiotherapy, but conventional models often produce anatomically implausible false posit...

Identifying and Characterizing Gallstone Disease from Clinical Narratives with Zero-shot Learning and Automated Prompt Optimization

We built and evaluated a zero-shot LLM pipeline with automated, task-aware prompt optimization to extract radiology and symptom fields for gallstone p...

Scale-Cascaded Diffusion Models for Super-Resolution in Medical Imaging

Diffusion models have been increasingly used as strong generative priors for solving inverse problems such as super-resolution in medical imaging. How...

Global mRNA 3'UTR lengthening in small-cell neuroendocrine carcinoma

Small-cell neuroendocrine carcinoma (SCNC) is a rare but highly malignant tumor subtype that primarily arises in the lung, also rarely in other organs...

Bridging the Applicator Gap with Data-Doping:Dual-Domain Learning for Precise Bladder Segmentation in CT-Guided Brachytherapy

Performance degradation due to covariate shift remains a major challenge for deep learning models in medical image segmentation. An open question is w...

Mechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer

Resistance to androgen receptor inhibitors remains a primary challenge in prostate cancer treatment, yet identifying synergistic co-therapies is hinde...

Learning temporal embeddings from electronic health records of chronic kidney disease patients

We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations wit...

From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images

Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domai...

Machine Learning Driven 'Therapy Calculator' for Self-Managed Digital Speech-Language Therapy for Individuals with Post-stroke Aphasia

Individuals with post-stroke aphasia live with long-term disabilities, yet they do not know whether they will improve their communication and cognitiv...

Generative modeling reveals the connection between cellular morphology and gene expression

The understanding of how transcriptional programs give rise to cellular morphology, and how morphological features reflect and influence cell identity...