Article

Semi-Supervised Learning in Prostate MRI Tumor Segmentation Approaches Fully-Supervised Performance on External Validation

2025-05-13

Abstract excerpt

<h4>Purpose</h4> To evaluate the diagnostic performance of semi-supervised learning models for aggressive prostate cancer segmentation on MRI compared to fully-supervised models trained with additional expert annotations. <h4>Materials and Methods</h4> We used 1500 MRI scans from the PI-CAI challenge training subset. Positive scans had 220 human and 205 AI-generated annotations. The mtU-Net (proposed teacher-stu...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 6423bd41-0293-5664-9f69-a2cc70612154
DOI: 10.1101/2025.05.13.25327456

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Semi-Supervised Learning in Prostate MRI Tumor Segmentation Approaches Fully-Supervised Performance on External ValidationDOI 10.1101/2025.05.13.25327456Read

Explainable AI for CNN-based Prostate Tumor Segmentation in Multi-parametric MRI Correlated to Whole Mount HistopathologySemantic proximity Read

Prostate MR Image Segmentation Using a Multi-Stage Network ApproachSemantic proximity Read

PSMASegmentator: Open-source, AI-based software for PSMA PET/CT automatic segmentation and prognostic biomarker extractionSemantic proximity Read

A generalizable deep learning model for preoperative ECE detection in prostate cancer: a multicenter studySemantic proximity Read

Rad-Path Correlation of Deep Learning Models for Prostate Cancer Detection on MRISemantic proximity Read

Prostate Cancer Detection in Bi-parametric MRI Using Deep Learning ModelSemantic proximity Read

Evaluating the Feasibility of AI-Predicted mpMRI Image Features for Predicting Prostate Cancer Aggressiveness: a Multicenter StudySemantic proximity Read

Urethra contours on MRI: multidisciplinary consensus educational atlas and reference standard for artificial intelligence benchmarkingSemantic proximity Read

Machine Learning-Based Radiomics Model to Predict Benign and Malignant PI-RADS v2.1 Category 3 lesions : A Retrospective Multi-center StudySemantic proximity Read

Development of a High-Performance Multiparametric MRI Oropharyngeal Primary Tumor Auto-Segmentation Deep Learning Model and Investigation of Input Channel Effects: Results from a Prospective Imaging RegistrySemantic proximity Read

A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological imagesSemantic proximity Read

Automatic Segmentation of Prostate Magnetic Resonance Imaging Using Generative Adversarial NetworksSemantic proximity Read