Urology

Can open source large language models be used for tumor documentation in Germany? -- An evaluation on urological doctors' notes

Tumor documentation in Germany is largely done manually, requiring reading patient records and entering data into structured databases. Large language models (LLMs) could potentially enhance this process by improving efficiency and reliability. This evaluation tests eleven different open source LLMs with sizes ranging from 1-70 billion model parameters on three basic tasks of the tumor documenta...

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout

Monte-Carlo (MC) Dropout provides a practical solution for estimating predictive distributions in deterministic neural networks. Traditional dropout, applied within the signal space, may fail to account for frequency-related noise common in medical imaging, leading to biased predictive estimates. A novel approach extends Dropout to the frequency domain, allowing stochastic attenuation of signal ...

[Predicting Intensive Care Unit Mortality in Patients With Heart Failure Combined With Acute Kidney Injury Using an Interpretable Machine Learning Model: A Retrospective Cohort Study].

OBJECTIVE: Heart failure (HF) complicated by acute kidney injury (AKI) significantly impacts patient outcomes, and it is crucial to make early predict...

Comparative Analysis of Hand-Crafted and Machine-Driven Histopathological Features for Prostate Cancer Classification and Segmentation

Histopathological image analysis is a reliable method for prostate cancer identification. In this paper, we present a comparative analysis of two ap...

Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging

Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,...

FedSemiDG: Domain Generalized Federated Semi-supervised Medical Image Segmentation

Medical image segmentation is challenging due to the diversity of medical images and the lack of labeled data, which motivates recent developments i...

Evaluating unsupervised contrastive learning framework for MRI sequences classification

The automatic identification of Magnetic Resonance Imaging (MRI) sequences can streamline clinical workflows by reducing the time radiologists spend...

Salient Region Matching for Fully Automated MR-TRUS Registration

Prostate cancer is a leading cause of cancer-related mortality in men. The registration of magnetic resonance (MR) and transrectal ultrasound (TRUS)...

RadHop-Net: A Lightweight Radiomics-to-Error Regression for False Positive Reduction In MRI Prostate Cancer Detection

Clinically significant prostate cancer (csPCa) is a leading cause of cancer death in men, yet it has a high survival rate if diagnosed early. Bi-par...

ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer

Prostate cancer, a growing global health concern, necessitates precise diagnostic tools, with Magnetic Resonance Imaging (MRI) offering high-resolut...

Self-supervised learning enables unbiased patient characterization from multiplexed cancer tissue microscopy images

Multiplexed immunofluorescence microscopy offers detailed insights into the spatial architecture of cancer tissue. However, classical single-cell anal...

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...

MACGA: Multi-scale Adaptive Convolution with Graph Attention for LncRNA–Disease Association Prediction

Accurate prediction of lncRNA-disease associations (LDAs) is crucial for understanding complex disease mechanisms and advancing precision medicine. Ex...

Deep-learning triage of 3D pathology datasets for comprehensive and efficient pathologist assessments

Standard-of-care slide-based 2D histopathology severely undersamples spatially heterogeneous tissue specimens, with each thin 2D section representing ...

Detection of prostate cancer in 3D pathology datasets via generative immunolabeling

Recent advancements in nondestructive 3D pathology offer a complement to standard histology by enabling comprehensive volumetric analyses of intact cl...

Deep learning-based image quantification of epithelial cell shapes and its application to polycystic kidney disease

Cell shape is a fundamental determinant of tissue architecture and organ function. In epithelial tissues, cytoskeletal organization and tight junction...

SpaPheno: Linking Spatial Transcriptomics to Clinical Phenotypes with Interpretable Machine Learning

Linking spatial transcriptomic data to clinically relevant phenotypes is essential for advancing spatially informed precision oncology. Here, we prese...

Evaluating BOLD functional MRI biophysical simulation approaches: impact of vascular geometry, magnetic field calculations, and water diffusion models

Biophysical simulations have guided the development of blood oxygenation level-dependent (BOLD) functional MRI (fMRI) acquisitions and signal models t...