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104: DigiPath Digest #12 (AI in Pathology: From Retinal Screening to Global Cancer Diagnostics + my new AI Co-Hosts)

Episode 104 Published 1Β year, 8Β months ago
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In this episode of DigiPath Digest you will learn about the development of AI models for glaucoma screening using fundus images, the use of AI in detecting metastatic deposits in colorectal cancer, and leveraging immunofluorescence data to reduce pathologist annotation requirements.Β 

Dr. Aleks also invited two AI Co-hosts and shared personal reflections on AI's role in the industry and invites feedback from listeners on AI-generated content.

00:00 Introduction to the Livestream Disaster
00:24 AI to the Rescue: Enhancing Audio Quality
00:38 Meet the AI Co-Hosts
01:04 Welcome to the Digital Pathology Podcast
01:30 Technical Difficulties and Audience Interaction
02:49 Exploring AI in Veterinary Medicine
04:34 Hybrid Convolutional Neural Network for Glaucoma Screening
07:49 Model for Detecting Metastatic Deposits in Lymph Nodes
11:23 Leveraging Immunofluorescence Data for Lung Tumor Segmentation
18:05 AI-Generated Content and Future Plans
21:37 AI Co-Hosts Take Over
32:42 Conclusion and Audience Feedback

TODAY'S EPISODES RESOURCES
πŸ“° Hybrid convolutional neural network optimized with an artificial algae algorithm for glaucoma screening using fundus images
πŸ”—https://pubmed.ncbi.nlm.nih.gov/39301801/

πŸ“° Automatic segmentation of esophageal cancer, metastatic lymph nodes and their adjacent structures in CTA images based on the UperNet Swin network
πŸ”—https://pubmed.ncbi.nlm.nih.gov/39300922/

πŸ“° Retrosynthetic analysis via deep learning to improve pilomatricoma diagnoses
πŸ”—https://pubmed.ncbi.nlm.nih.gov/39298885/

πŸ“° Obesity-Associated Breast Cancer: Analysis of Risk Factors and Current Clinical Evaluation
πŸ”— https://pubmed.ncbi.nlm.nih.gov/39287872/

πŸ“° Model for detecting metastatic deposits in lymph nodes of colorectal carcinoma on digital/ non-WSI images
πŸ”— https://pubmed.ncbi.nlm.nih.gov/39285483/

πŸ“° Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning
πŸ”— https://pubmed.ncbi.nlm.nih.gov/39284813/

πŸ“° Bayesian Landmark-based Shape Analysis of Tumor Pathology Images
πŸ”— https://pubmed.ncbi.nlm.nih.gov/39280355/

πŸ“° Globalization of a telepathology network with artificial intelligence applications in Colombia: The GLORIA program study protocol
πŸ”— https://pubmed.ncbi.nlm.nih.gov/39280257/

πŸ“° Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy
πŸ”— https://pubmed.ncbi.nlm.nih.gov/39277586/

πŸ“° Sex differences in sociodemographic, clinical, and laboratory variables in childhood asthma: A birth cohort study
πŸ”— https://pubmed.ncbi.nlm.nih.gov/39019434/

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