Part 2/11:

• AI-assisted diagnosis and disease detection (such as identifying tumors or infections)

• Image segmentation, which involves precisely delineating regions of interest like tumors or inflamed tissues

Participants were shown benchmark images featuring input data, automatic segmentation results, and manual expert annotations. These side-by-side comparisons demonstrated how AI models, especially deep learning architectures, can produce results closely matching those of experienced radiologists, thereby streamlining workflows and reducing manual effort.

Deep Learning Architectures Powering Medical Image Segmentation

A core part of the discussion centered around the architectures enabling these advancements: