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Managing Oneself: A Lesson in Herding Cats (except it's actually only one cat...and the cat has acute time management issues that the vet is starting to think are less of a professional weakness and more of an inherent character flaw)

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In this semester's final blog post, we celebrate an as yet distant dream, now fulfilled. After many years, the day has finally come where I can turn my relentless, self-deprecating inner dialogue over for academic validation. We've reached the central slice in my Venn Diagram of favourite things. I'm too excited to even spin up my usual long-winded introduction. Let's just skip straight to the free therapeutic outlet! 🥳
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rottentomatoes.com/a/segment_anything_in_medical_images: An Exercise in Peer Review! 🍅⭐

Hello! It's that time of the month again! FYI: Someone mistook the due date of this blog post and thought it was required before the next seminar! Sorry!! Our topic this time around is peer reviewing scientific subject material. Reviews are a quality assurance mechanism to help editors decide whether a given manuscript should be accepted to a journal or conference based on peer and editorial standards.  Anecdotally, whether standards are consistently, justifiably applied during the feedback process seems to be hit-or-miss. To keep things nice and clean, we'll try for a hit with today's blog entry by applying the review template from the International Joint Conference on Artificial Intelligence ( IJCAI ) , assigning 1-10 star/s for each non-comment criterion ... and yes, it is taking a lot of self-discipline to not do the fun thing - devolving into baseless critique and juicy back-and-forth  à  la OpenReview comment sections.  Thanks for asking. As per usual, Ma et....
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Attempting to Understand Scientific Subject Matter (feat. MedSAM)

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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...
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