Integrating AI and Pathology: A Novel Approach to Multiplex Data Segmentation and Classification
Overview: Explores the integration of artificial intelligence (AI) in pathology to enhance segmentation and classification of multiplexed imaging data.
Tutorial: Provides a step-by-step guide on applying AI algorithms to multiplexed pathology datasets, demonstrating how to effectively segment and classify complex tissue structures.
Benefits: Highlights how this AI-driven approach improves the accuracy and efficiency of data analysis in pathology, leading to more precise diagnostics and personalized treatment strategies.
Segment Anything Model In QuPath
Overview: Demonstrating the integration of the Segment Anything Model (SAM) within QuPath for advanced image segmentation.
Tutorial: Step-by-step guide on installing and applying SAM to segment tissue, nuclei, and other structures.
Overview: Explores the integration of digital pathology with virtual reality (VR) technology using QuPath and an Oculus Rift S headset.
Tutorial: Demonstrates how to navigate and interact with pathology images in a VR environment, providing a hands-on experience of viewing and analyzing digital slides.
Benefits: Highlights the immersive experience VR offers, potentially enhancing the way pathologists and researchers interact with and interpret complex tissue structures.
Using ChatGPT (And Pyvips) To Convert Proprietary Images Into Open Source Formats
Overview: Explores the process of converting proprietary image formats into open-source formats using ChatGPT and the pyvips library.
Tutorial: Provides a step-by-step guide on utilizing ChatGPT to generate pyvips scripts for image conversion, demonstrating the practical application of AI in streamlining image processing tasks.
Benefits & Cautions: Highlights how this approach facilitates greater accessibility and interoperability of image data. However, it also emphasizes that AI-generated code may not always be accurate and should not replace the expertise of a developer. The video compares AI-generated scripts with those crafted by libvips developers, illustrating that human-developed code often yields superior results.
Automated Quantification Of Mycelium Area On A Petri Dish
Overview: Demonstrates a method to automatically quantify the area occupied by mycelium on a Petri dish using image analysis techniques.
Tutorial: Provides a step-by-step guide on processing images of Petri dishes to accurately measure mycelium coverage, utilizing open-source tools and scripts.
Benefits: Highlights how automation enhances the accuracy and efficiency of mycelium area quantification, reducing manual measurement errors and saving time in fungal growth studies.
We use cookies to analyze website traffic and optimize your website experience. By accepting our use of cookies, your data will be aggregated with all other user data.