Samir Harake

23 posts

Samir Harake

Samir Harake

@SamirHarake

Med Student @UMichMedSchool | @UMich '19 @MichiganRoss '20

Katılım Ocak 2021
179 Takip Edilen107 Takipçiler
Samir Harake retweetledi
Todd Hollon
Todd Hollon@ToddCHollon·
@mlins_lab PhD all-star, Yiwei Lyu, presenting his paper at @RealAAAI on Restorative Step-Calibrated Diffusion. His paper solves the problem of restoring biomedical images with variable and unknown amounts of data degradation. Checkout the preprint: arxiv.org/abs/2403.13680
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Michigan Neurosurgery
Michigan Neurosurgery@umichneuro·
🌟 Attention #SpineSummit2025 attendees! 🌟 As you plan your schedule, make sure to note the sessions featuring @umichneuro residents. Their contributions are insightful and inspiring, so plan accordingly and don’t miss out on their sessions! 📅 #GoBlue @spinesection
Michigan Neurosurgery tweet mediaMichigan Neurosurgery tweet media
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Todd Hollon
Todd Hollon@ToddCHollon·
🚀 Proud to introduce #FastGlioma: the first foundation model enabling rapid, accurate detection of brain tumor infiltration during surgery, in under 10 seconds. With FastGlioma, we’re minimizing the risk of residual tumor and enhancing outcomes for glioma patients. This work sets a new standard in real-time, microscopic-level detection, powered by AI in healthcare. Kudos to the incredible team @mlins, @HerveyJumper, @DanOrringerMD, @InvenioImaging, @CameloPiragua! Read the full paper in @Nature: nature.com/articles/s4158…
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Todd Hollon
Todd Hollon@ToddCHollon·
@__chengjia__ presenting at UM Bioinformatics BISTRO conference. Great talk on single cell optical phenotyping! @mlins_lab
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Max Lu
Max Lu@MYLu97·
4/7 📊 Hou & Jiang et al. present SPT, a framework for learning self-supervised slide representations, which is consistently able to learn strong slide-level features across a variety of encoders, including UNI. arXiv: arxiv.org/abs/2402.06188 ⚡️This is the first work to investigate slide pretraining across a diverse variety of ROI encoders. The analyses in Hou & Jiang et al. suggest that slide pretraining provide the biggest performance gains in less powerful ROI encoders, with least benefit in HiDisc and UNI. They also show the importance of further finetuning as well, which can yield as big of an improvement as slide pretraining. 💭 We believe more development needs to happen in self-supervised slide encoders than ROI encoders. Few works in this area, and where most of the technical advancements need to be made 🔥
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Samir Harake
Samir Harake@SamirHarake·
Integrated into med education should be more instruction on the business side of medicine: who are the stakeholders (hospitals, insurance, PBM, pharma), what are their incentives, and how does this converge into the costs our patients bear? Clear understandings --> holisitic care
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