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Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Related Experiment Video

Updated: Jul 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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DEPAS: De-novo Pathology Semantic Masks using a Generative Model.

Ariel Larey, Nati Daniel, Eliel Aknin

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Generating synthetic histological images with controlled cellular features is crucial for unbiased AI in digital pathology. Our novel DEPAS model creates high-quality semantic masks, enabling scalable, photorealistic image synthesis for improved diagnostics.

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    Area of Science:

    • Digital pathology
    • Artificial intelligence
    • Computational biology

    Background:

    • AI in digital pathology offers automation but faces challenges with biased datasets due to tissue variability and labeling needs.
    • Synthetic histological images are a promising solution for debiasing datasets, requiring photorealistic generation and controllable cellular features.

    Purpose of the Study:

    • To introduce DEPAS (De-novo Pathology Semantic Masks), a scalable generative model for creating high-quality semantic masks of tissue structures.
    • To demonstrate the utility of DEPAS-generated masks in producing photorealistic synthetic histology images with controllable cellular features for AI training.

    Main Methods:

    • Developed DEPAS, a scalable generative model for de-novo semantic mask generation.
    • Utilized image translation models to convert semantic masks into photorealistic histology images.
    • Generated multi-label semantic masks to control cellular feature distribution in synthetic images.

    Main Results:

    • DEPAS successfully generated high-resolution, state-of-the-art semantic masks for skin, prostate, and lung tissues.
    • Synthetic histology images of cancer were produced using DEPAS masks and image translation, showcasing realism across different staining techniques.
    • On-demand cellular features were generated in synthetic histology images by leveraging multi-label semantic masks.

    Conclusions:

    • DEPAS provides a scalable and effective solution for generating controlled synthetic histological images.
    • This approach addresses the limitations of real-world datasets, paving the way for more generalizable AI algorithms in digital pathology.
    • The ability to control semantic information in synthetic images enhances their utility for AI model development and validation.