• 99: DigiPath Digest #7 (Exploring AI-driven advances in digital pathology from T-cell signatures to synthetic images)

  • Aug 9 2024
  • Duración: 44 m
  • Podcast

99: DigiPath Digest #7 (Exploring AI-driven advances in digital pathology from T-cell signatures to synthetic images)

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    In this episode of DigiPath Digest, broadcasting from Poland, we delve into advances in digital pathology, including AI applications in bone marrow evaluation, classification of hematology cells, and the use of synthetic images for data augmentation. Additionally, we review a survey on pathologists' perceptions of ChatGPT and consider the feasibility of GANs for enhancing medical image analysis.

    00:00 Welcome and Troubleshooting from Poland
    00:21 Live Stream Challenges and Conference Details
    02:21 Digital Pathology Podcast Introduction
    02:51 Technical Difficulties and Audience Interaction
    06:18 Exploring Digital Pathology Papers
    06:43 Advances in Bone Marrow Evaluation
    09:03 AI in Hematology and Pathology
    12:28 Colorectal Cancer Prognostication
    19:34 Pan-Cancer Xenograft Repository
    25:16 ChatGPT and Pathology Survey
    30:55 Synthetic Image Generation in Pathology
    36:35 Upcoming Conferences and Courses
    42:27 Closing Remarks and Future Plans

    THE ABSTRACTS WE COVERED TODAY

    📄 Advances in Bone Marrow Evaluation
    https://pubmed.ncbi.nlm.nih.gov/39089749/

    📄 Digital Imaging and AI Pre-classification in Hematology
    https://pubmed.ncbi.nlm.nih.gov/39089746/

    📄 Evaluation of CD3 and CD8 T-Cell Immunohistochemistry for Prognostication and Prediction of Benefit From Adjuvant Chemotherapy in Early-Stage Colorectal Cancer Within the QUASAR Trial
    https://pubmed.ncbi.nlm.nih.gov/39083705/

    📄 A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
    A survey analysis of the adoption of large language models among pathologists
    https://pubmed.ncbi.nlm.nih.gov/39082680/

    📄 Clinical-Grade Validation of an Autofluorescence Virtual Staining System with Human Experts and a Deep Learning System for Prostate Cancer

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