MedSapiens: Repurposing Human Pose Tech for Smarter Medical Landmarks

Kari Jaaskelainen

07 Nov 2025 — 1 min read

What if human pose tech could guide doctors in scans?

MedSapiens takes a simple but bold idea: adapt a powerful human pose foundation model to find anatomical landmarks—key points—in medical images.

Built by fine-tuning Sapiens across multiple medical datasets.
Sets a new state of the art in success detection rate (SDR): up to 5.26% better than generalist models and up to 21.81% better than specialist models.
Works even with few labels, improving few-shot SDR by 2.69%.

Why it matters: Landmark detection underpins many clinical and research workflows—from aligning scans to measuring structures. By reusing a pose model tuned for precise spatial localization, MedSapiens shows that strong priors can outperform bespoke, task-specific designs.

Paper: http://arxiv.org/abs/2511.04255v1
Code & weights: https://github.com/xmed-lab/MedSapiens

Authors: Marawan Elbatel, Anbang Wang, Keyuan Liu, Kaouther Mouheb, Enrique Almar-Munoz, Lizhuo Lin, Yanqi Yang, Karim Lekadir, Xiaomeng Li

Paper: http://arxiv.org/abs/2511.04255v1

Register: https://www.AiFeta.com

AI MedicalImaging ComputerVision PoseEstimation FoundationModels OpenSource Research Healthcare

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

GDPR compliance is more than checkboxes. A new roadmap from the Privatech project shows how automation and machine learning can help companies and law firms assess—and even generate—privacy compliance. * Shift the focus to data processors’ real workflows: drafting policies, mapping data uses, documenting decisions. * Break compliance into machine-ready

FPGAs for Faster, Leaner Deep Learning: A Review of CNN Accelerators

Deep learning drives image search, robots, and medical scans. Most systems lean on CPUs and GPUs. This review asks: what if we run convolutional neural networks (CNNs) on FPGAs—reconfigurable chips you can tailor to the model? * Why FPGAs: custom dataflows, low latency, and strong energy efficiency—great for cameras,

Dynamic-K: Recommendations That Know When to Stop

Most apps show a fixed number of “top” items—say 10 movies or 20 products—assuming there are always enough good options. But that’s not always true: sometimes there are few relevant items, or some users are extra picky. The result? Filler recommendations. Dynamic-K flips the script. Instead of

Teaching chatbots to stop contradicting themselves (DECODE)

Teaching chatbots to stop contradicting themselves Ever had a bot say one thing, then the opposite a few turns later? This study introduces DECODE—a new task and dataset for spotting contradictions in everyday conversations, drawn from both human-human and human-bot chats. * New data beats existing natural language inference (NLI)

What if human pose tech could guide doctors in scans?

Read more

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

FPGAs for Faster, Leaner Deep Learning: A Review of CNN Accelerators

Dynamic-K: Recommendations That Know When to Stop

Teaching chatbots to stop contradicting themselves (DECODE)