Attempting to Understand Scientific Subject Matter (feat. MedSAM)
Following on from previous posts, "Segment Anything in Medical Images" will reprise its starring role in this month's blog entry - our topic now turning from writing style to subject matter. Specifically, the critical reading & understanding of a scholarly work's content and its value in the context of the relevant sub-field. Context & Problem Foundation Models (FMs) are notably large (i.e., having many parameters), deep (i.e., having many layers) neural networks trained on substantial amounts of data so they can be used across many, varied tasks. This general-purpose applicability constitutes their ⭐ grand appeal ⭐. A combination of architecture, training dataset properties, and training protocol means FMs can be put to work for a range of tasks out-of-the-box or with minimal fine-tuning instead of having to develop, train, and deploy an independent model for each use case. One such FM - Meta AI's Segment Anything (SAM) model - shook the sub-field of i...