ArticleRadiology. Artificial intelligence2023
Semisupervised Learning with Report-guided Pseudo Labels for Deep Learning-based Prostate Cancer Detection Using Biparametric MRI.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 52 citations in OpenAlex.
- Report Supervision.Medical image analysis · 2026Article
- The Role of Artificial Intelligence in Optimizing Diagnosis in Prostate Cancer-A Narrative Review.Journal of clinical medicine · 2026Review
- Deep learning-based prostate cancer diagnosis on MRI with hip prostheses: artifact and sequence effects.Abdominal radiology (New York) · 2026Article
- Semi-supervised learning in prostate MRI tumor detection approaches fully supervised performance on external validation.European radiology · 2026Article
- Dissecting self-supervised learning strategies for transfer learning in MRI prostate cancer diagnosis.Scientific reports · 2026Article
- Evaluation of AI for prostate cancer detection in biparametric-MRI screening population data.European radiology · 2026Article
- 3D-StyleGAN2-ADA: Volumetric Synthesis of Realistic Prostate T2W MRI.Journal of imaging · 2026Article
- Deep Learning Based Computer-Aided Detection of Prostate Cancer Metastases in Bone Scintigraphy: An Experimental Analysis.Journal of imaging · 2026Article
- Artificial Intelligence in Prostate MRI: Addressing Current Limitations Through Emerging Technologies.Journal of magnetic resonance imaging : JMRI · 2026Review
- External evaluation of an open-source deep learning model for prostate cancer detection on bi-parametric MRI.European radiology · 2026Article
- MRI-based patient selection for active surveillance in prostate cancer using U-Found: a generalized deep learning model.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric.European radiology · 2025Article
- Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment.Asian journal of urology · 2025Review
- Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges-A Narrative Review.Journal of imaging · 2025Review
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- Evaluation of a deep learning prostate cancer detection system on biparametric MRI against radiological reading.European radiology · 2025Article
- Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.Scientific reports · 2025Article
- An overview of utilizing artificial intelligence in localized prostate cancer imaging.Expert review of medical devices · 2025Review
- ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases.Medical image analysis · 2025Article
- Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.European radiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To evaluate a novel method of semisupervised learning (SSL) guided by automated sparse information from diagnostic reports to leverage additional data for deep learning-based malignancy detection in patients with clinically significant prostate cancer. Materials and Methods: This retrospective study included 7756 prostate MRI examinations (6380 patients) performed between January 2014 and December 2020 for model development. An SSL method, report-guided SSL (RG-SSL), was developed for detection of clinically significant prostate cancer using biparametric MRI. RG-SSL, supervised learning (SL), and state-of-the-art SSL methods were trained using 100, 300, 1000, or 3050 manually annotated examinations. Performance on detection of clinically significant prostate cancer by RG-SSL, SL, and SSL was compared on 300 unseen examinations from an external center with a histopathologically confirmed reference standard. Performance was evaluated using receiver operating characteristic (ROC) and free-response ROC analysis. Results: At 100 manually annotated examinations, mean examination-based diagnostic area under the ROC curve (AUC) values for RG-SSL, SL, and the best SSL were 0.86 ± 0.01 (SD), 0.78 ± 0.03, and 0.81 ± 0.02, respectively. Lesion-based detection partial AUCs were 0.62 ± 0.02, 0.44 ± 0.04, and 0.48 ± 0.09, respectively. Examination-based performance of SL with 3050 examinations was matched by RG-SSL with 169 manually annotated examinations, thus requiring 14 times fewer annotations. Lesion-based performance was matched with 431 manually annotated examinations, requiring six times fewer annotations. Conclusion: RG-SSL outperformed SSL in clinically significant prostate cancer detection and achieved performance similar to SL even at very low annotation budgets.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.