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99: DigiPath Digest #7 (Exploring AI-driven advances in digital pathology from T-cell signatures to synthetic images)
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Contenuto fornito da Aleksandra Zuraw, DVM, PhD, Aleksandra Zuraw, and DVM. Tutti i contenuti dei podcast, inclusi episodi, grafica e descrizioni dei podcast, vengono caricati e forniti direttamente da Aleksandra Zuraw, DVM, PhD, Aleksandra Zuraw, and DVM o dal partner della piattaforma podcast. Se ritieni che qualcuno stia utilizzando la tua opera protetta da copyright senza la tua autorizzazione, puoi seguire la procedura descritta qui https://it.player.fm/legal.
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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