@MedARC_AI

Medical AI Research Center (MedARC) Unlocking new possibilities in medical AI research. Supported by @SophontAI Founded by @iScienceLuvr

medarc.ai
Joined January 2023
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Today, we're excited to announce our first research competition! INTRODUCING NANOPATH: a framework and challenge to train the best pathology foundation model in just 1 hour! A quick thread on why we made this challenge and how to participate!
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This is TODAY (in 5 hours!) Come join!!
MedARC@MedARC_AIOct 2
Continuing our Journal Club series on Monday October 5th at 11am PT! Ben Tang (researcher at Duke) will be presenting his paper "Pretraining for Sample-Efficient Neural Interfaces" To attend, join the Discord and check our website!
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Continuing our Journal Club series on Monday October 5th at 11am PT! Ben Tang (researcher at Duke) will be presenting his paper "Pretraining for Sample-Efficient Neural Interfaces" To attend, join the Discord and check our website!
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journal club info: sophont.med/journal-club/

Sophont Journal Club

Research presentations and discussions from the Sophont and MedARC community.

sophont.med
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I'm excited to share Medmarks was accepted to the NeurIPS datasets and benchmarks track! 🥳 See you at Sydney!
Sophont
@SophontAIMay 12
We're excited to release Medmarks v1.0 + a technical report! This is an update to our Medmarks benchmark suite, the largest open-source automated suite for evaluating the medical capabilities of LLMs. We added 10 benchmarks (20→30) and 15 models (46→61) to the leaderboard!
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A fully autonomous agent topped the nanopath leaderboard earlier last week. Shortly afterwards, a human-created submission topped it again... A competition between humans and agents ensues!
AutoTrust
@AutoTrustAISep 9
RSI, proven again. 🧠🔬 ScienceGuru + Guru Turbo 1.0 is now the #1 maintainer-validated trainable run on MedARC's NanoPath — an open, FLOP-capped benchmark for training pathology foundation models, competing against recipes from human research labs (MSK, Oregon, UBC, and others). Our autonomous research system ran the full loop itself: generated hypotheses, wrote the code, ran controlled experiments on a single H100, and iterated its way to the top recipe. Score: 0.6597 — a +0.025 jump over the prior best community recipe, with gains across 5 of 6 metric families (classification, segmentation, progression, mutation, survival). This is the second independent, maintainer-validated #1 for our recursive self-improvement stack — after Autoresearch@Home. Different scientific domain. Zero recipe transfer. Same system. The harness generalizes. That's the RSI claim in falsifiable form: an AI that improves AI, on open leaderboards anyone can check — fully open source, as the benchmark requires: github.com/AutoTrustAI/nanop… Recursive self-improvement isn't a slogan. It's a leaderboard entry. More domains coming. @MedARC_AI @ScienceGuruAI
RSI, proven again. 🧠🔬 ScienceGuru + Guru Turbo 1.0 is now the #1 maintainer-validated trainable run on MedARC's NanoPath — an open, FLOP-capped benchmark for training pathology foundation models, competing against recipes from human research labs (MSK, Oregon, UBC, and others). Our autonomous research system ran the full loop itself: generated hypotheses, wrote the code, ran controlled experiments on a single H100, and iterated its way to the top recipe. Score: 0.6597 — a +0.025 jump over the prior best community recipe, with gains across 5 of 6 metric families (classification, segmentation, progression, mutation, survival). This is the second independent, maintainer-validated #1 for our recursive self-improvement stack — after Autoresearch@Home. Different scientific domain. Zero recipe transfer. Same system. The harness generalizes. That's the RSI claim in falsifiable form: an AI that improves AI, on open leaderboards anyone can check — fully open source, as the benchmark requi

ALT RSI, proven again. 🧠🔬 ScienceGuru + Guru Turbo 1.0 is now the #1 maintainer-validated trainable run on MedARC's NanoPath — an open, FLOP-capped benchmark for training pathology foundation models, competing against recipes from human research labs (MSK, Oregon, UBC, and others). Our autonomous research system ran the full loop itself: generated hypotheses, wrote the code, ran controlled experiments on a single H100, and iterated its way to the top recipe. Score: 0.6597 — a +0.025 jump over the prior best community recipe, with gains across 5 of 6 metric families (classification, segmentation, progression, mutation, survival). This is the second independent, maintainer-validated #1 for our recursive self-improvement stack — after Autoresearch@Home. Different scientific domain. Zero recipe transfer. Same system. The harness generalizes. That's the RSI claim in falsifiable form: an AI that improves AI, on open leaderboards anyone can check — fully open source, as the benchmark requi

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RSI, proven again. 🧠🔬 ScienceGuru + Guru Turbo 1.0 is now the #1 maintainer-validated trainable run on MedARC's NanoPath — an open, FLOP-capped benchmark for training pathology foundation models, competing against recipes from human research labs (MSK, Oregon, UBC, and others). Our autonomous research system ran the full loop itself: generated hypotheses, wrote the code, ran controlled experiments on a single H100, and iterated its way to the top recipe. Score: 0.6597 — a +0.025 jump over the prior best community recipe, with gains across 5 of 6 metric families (classification, segmentation, progression, mutation, survival). This is the second independent, maintainer-validated #1 for our recursive self-improvement stack — after Autoresearch@Home. Different scientific domain. Zero recipe transfer. Same system. The harness generalizes. That's the RSI claim in falsifiable form: an AI that improves AI, on open leaderboards anyone can check — fully open source, as the benchmark requires: github.com/AutoTrustAI/nanop… Recursive self-improvement isn't a slogan. It's a leaderboard entry. More domains coming. @MedARC_AI @ScienceGuruAI
RSI, proven again. 🧠🔬 ScienceGuru + Guru Turbo 1.0 is now the #1 maintainer-validated trainable run on MedARC's NanoPath — an open, FLOP-capped benchmark for training pathology foundation models, competing against recipes from human research labs (MSK, Oregon, UBC, and others). Our autonomous research system ran the full loop itself: generated hypotheses, wrote the code, ran controlled experiments on a single H100, and iterated its way to the top recipe. Score: 0.6597 — a +0.025 jump over the prior best community recipe, with gains across 5 of 6 metric families (classification, segmentation, progression, mutation, survival). This is the second independent, maintainer-validated #1 for our recursive self-improvement stack — after Autoresearch@Home. Different scientific domain. Zero recipe transfer. Same system. The harness generalizes. That's the RSI claim in falsifiable form: an AI that improves AI, on open leaderboards anyone can check — fully open source, as the benchmark requi

ALT RSI, proven again. 🧠🔬 ScienceGuru + Guru Turbo 1.0 is now the #1 maintainer-validated trainable run on MedARC's NanoPath — an open, FLOP-capped benchmark for training pathology foundation models, competing against recipes from human research labs (MSK, Oregon, UBC, and others). Our autonomous research system ran the full loop itself: generated hypotheses, wrote the code, ran controlled experiments on a single H100, and iterated its way to the top recipe. Score: 0.6597 — a +0.025 jump over the prior best community recipe, with gains across 5 of 6 metric families (classification, segmentation, progression, mutation, survival). This is the second independent, maintainer-validated #1 for our recursive self-improvement stack — after Autoresearch@Home. Different scientific domain. Zero recipe transfer. Same system. The harness generalizes. That's the RSI claim in falsifiable form: an AI that improves AI, on open leaderboards anyone can check — fully open source, as the benchmark requi

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crazy that anyone can use AI agents to train foundation models to help tackle cancer :) labless.dev/nano-projects/na…
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Be sure to subscribe to the MedARC YouTube channel! Talks from our journal club are all uploaded there :)
Gabriel Clark@GcabrielclarkAug 14
Replying to @iScienceLuvr @TimDarcet @MedARC_AI
You should post this on YouTube! I’d love to forward this to people not on X.
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Meta AI researcher who created DINOv2 explains modern self-supervised learning! Also introduces a new approach called CAPI @TimDarcet gave a brilliant talk about his research at the @MedARC_AI journal club last month. Very clear and information-dense, I learned a lot from his talk. Give it a watch!
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Starting now!!!
MedARC@MedARC_AIJul 28
We are hosting yet another awesome Journal Club presentation on July 30th at 8:30am PT! @TimDarcet (researcher at Meta) will be presenting his paper "CAPI: Cluster and Predict Latent Patches for Improved Masked Image Modeling" Join the Discord and check our website!
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Reminder this is in 12 hrs!! Do not miss!
MedARC@MedARC_AIJul 28
We are hosting yet another awesome Journal Club presentation on July 30th at 8:30am PT! @TimDarcet (researcher at Meta) will be presenting his paper "CAPI: Cluster and Predict Latent Patches for Improved Masked Image Modeling" Join the Discord and check our website!
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I'm pretty excited about this... Tim is, in my opinion, a legend in the field of modern self-supervised learning: he developed DINOv2, was involved in DINOv3, and invented ViT registers. He has a recent paper formulating a new SSL approach called CAPI, and he'll be presenting it in our journal club on Thursday! At @MedARC_AI we're very interested in exploring alternative SSL approaches apart from the standard DINO and MAE paradigms. We believe innovations in SSL training will lead to better medical imaging foundation models! So we're looking forward to learning more about CAPI, come join us!!
MedARC@MedARC_AIJul 28
We are hosting yet another awesome Journal Club presentation on July 30th at 8:30am PT! @TimDarcet (researcher at Meta) will be presenting his paper "CAPI: Cluster and Predict Latent Patches for Improved Masked Image Modeling" Join the Discord and check our website!
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We are hosting yet another awesome Journal Club presentation on July 30th at 8:30am PT! @TimDarcet (researcher at Meta) will be presenting his paper "CAPI: Cluster and Predict Latent Patches for Improved Masked Image Modeling" Join the Discord and check our website!
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Journal club info: sophont.med/journal-club

Sophont Journal Club

Research presentations and discussions from the Sophont and MedARC community.

sophont.med
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MedARC retweeted
Sophont
@SophontAIJul 7
SOPHONT at ICML: Posters: 1. CortexMAE (MRI foundation model) - Wed July 8th 2:30pm Hall A #806 2. Medmarks (LLM evals) - Sat July 10th 11:05am S317 Social: Bits and Atoms Lunch - Fri July 10th 12pm (link in reply) STOP BY AT OUR POSTERS AND SOCIAL!
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Journal Club presentation TODAY at 8:30am PT! We will be discussing the FINO paper from Meta: "Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Have" Join the Discord and check our calendar!
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Links with more details: leaderboard: labless.dev/nano-projects/na… blog post: sophont.med/blog/nanopath code: github.com/MedARC-AI/nanopat…
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We hope nanopath can serve as an accessible test-bench to explore novel self-supervised approaches and fosters innovation in the space!
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