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Related Experiment Video

Updated: Jan 9, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Soft Annotations versus Pixel-Based Segmentation Masks of Prostate Anatomies: The Effect of Annotation Type on

Eleftherios Trivizakis, Georgios S Ioannidis, Katerina Nikiforaki

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces YOLOv8 deep learning models for prostate cancer detection using bounding boxes, achieving high accuracy for gland detection and improving lesion identification efficiency for large MRI datasets.

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    Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Oncology

    Background:

    • Prostate cancer detection faces challenges due to low MRI contrast, small lesions, and inter-observer variability.
    • Magnetic Resonance Imaging (MRI) variability complicates deep learning segmentation tasks.
    • Bounding box detection offers an efficient alternative to precise segmentation for deep learning analysis.

    Purpose of the Study:

    • To evaluate YOLOv8 deep learning architectures for prostate gland and neoplasm detection.
    • To compare the impact of bounding box annotations versus pixel-based annotations on imaging features.
    • To assess the influence of image quality on deep learning-based gland detection.

    Main Methods:

    • Utilized various YOLOv8 deep learning architectures for prostate cancer detection.
    • Performed gland detection and prostate lesion detection using bounding box annotations.
    • Analyzed the effect of image quality and annotation types on model performance.

    Main Results:

    • The prostate gland detection model achieved a mean Average Precision (mAP) of 95.8±1%.
    • The optimal prostate lesion detection model reached a mAP of 41.5±7%.
    • Demonstrated the impact of bounding boxes and image quality on detection accuracy.

    Conclusions:

    • Deep learning models, particularly YOLOv8, can effectively detect prostate glands and lesions using bounding boxes.
    • This approach can significantly accelerate the annotation process for large multi-modal MRI datasets in oncology.
    • The findings suggest a viable method to overcome annotation barriers and inter-observer variability in machine learning for prostate cancer research.