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Scalable Clinical Annotation with Location Evidence (SCALE).

Joeran S Bosma1, Luc Builtjes2, Anindo Saha3

  • 1Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands; Department of Health & Information Technology, Ziekenhuisgroep Twente, Almelo, The Netherlands; Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.

Computers in Biology and Medicine
|November 22, 2025
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Summary
This summary is machine-generated.

This study introduces SCALE, an automated method for creating large-scale annotated medical datasets. AI models trained with SCALE annotations show superior performance in detecting prostate cancer on MRI.

Keywords:
AnnotationDeep learningMRIMedical imagingProstate cancerWeakly supervised learning

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Area of Science:

  • Medical Imaging AI
  • Machine Learning in Radiology
  • Prostate Cancer Diagnostics

Background:

  • Deep learning for medical imaging requires large annotated datasets, which are difficult to obtain.
  • The global shortage of radiologists necessitates efficient AI development for medical image analysis.
  • Automated annotation methods are crucial for scaling AI development in healthcare.

Purpose of the Study:

  • To introduce SCALE (Scalable Clinical Annotation with Location Evidence), a fully automated method for generating voxel-level annotations.
  • To develop and train an optimized AI algorithm using large-scale datasets annotated with SCALE.
  • To evaluate the performance of AI models trained with SCALE annotations against other methods for prostate cancer detection on MRI.

Main Methods:

  • Developed SCALE, a method utilizing location priors from medical reports, biopsy coordinates, or anatomical sectors for automated annotation.
  • Annotated a large dataset of 17,896 cases using both SCALE and a count-based weakly semisupervised learning (CWSSL) method.
  • Trained and evaluated an optimized AI algorithm on datasets generated by SCALE and CWSSL, comparing performance against supervised learning and the PI-CAI Ensemble AI System.

Main Results:

  • The AI model trained on SCALE-annotated data achieved a case-level area under the receiver operating characteristic curve (AUC) of 0.856.
  • This performance was superior to models trained with supervised learning (AUC +0.012, p=0.02) and comparable to CWSSL (AUC +0.007, p=0.12).
  • The SCALE-trained model also showed a slight advantage over the PI-CAI Ensemble AI System (AUC +0.006).

Conclusions:

  • Automated, location-guided annotation with SCALE enables scalable development of AI for clinically significant prostate cancer detection on MRI.
  • The SCALE method surpasses previous annotation and AI training approaches, facilitating broader clinical deployment of AI tools.
  • This work demonstrates the potential of automated annotation to address data limitations in medical AI research and development.