iiinova — where your digital self keeps evolving

Reposted by Daniel Coelho de Castro

Fernando Pérez-García @fepegar.com · 30/01/2026

We are excited to release the weights of @msftresearch.bsky.social's COLIPRI, our 3D vision–language encoder for chest CT scans, on @hf.co 🤗 Model: aka.ms/colipri Demo: aka.ms/colipri-demo Paper: aka.ms/colipri-paper Why does COLIPRI matter? 🧵0/12 👇

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Daniel Coelho de Castro @dccastr0.bsky.social · 07/12/2025

Excited to speak shortly at the Medical Imaging at @euripsconf.bsky.social workshop in Copenhagen, where I'll share some insights from our @msftresearch.bsky.social team's journey "From medical image interpretation to scientific discovery" over the past couple of years.

Opening slide for a presentation titled "From medical image interpretation to scientific discovery"

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Reposted by Daniel Coelho de Castro

Stephanie Hyland @hylandsl.bsky.social · 18/07/2025

New work from my team! arxiv.org/abs/2507.12950 Intersecting mechanistic interpretability and health AI 😎 We trained and interpreted sparse autoencoders on MAIRA-2, our radiology MLLM. We found a range of human-interpretable radiology reporting concepts, but also many uninterpretable SAE features.

arxiv.org

Insights into a radiology-specialised multimodal large language model with sparse autoencoders

Interpretability can improve the safety, transparency and trust of AI models, which is especially important in healthcare applications where decisions often carry significant consequences. Mechanistic...

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Reposted by Daniel Coelho de Castro

NEJM AI @ai.nejm.org · 10/07/2025

Datasets, Benchmarks, and Protocols by D.C. de Castro et al.: PadChest-GR: A Bilingual Chest X-Ray Dataset for Grounded Radiology Report Generation nejm.ai/44rk1a1 @dccastr0.bsky.social #AI #MedSky #MLSky

Figure 1. Example of a Grounded Report from PadChest-GR.

Figure 2. Illustration of the Data-Curation Pipeline.

Figure 3. Screenshots of the Interface for Both Manual Annotation Stages.

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Reposted by Daniel Coelho de Castro

NEJM AI @ai.nejm.org · 03/07/2025

A novel dataset designed to train and evaluate grounded report generation models from chest x-ray images includes comprehensive sentence-level bounding-box annotations for all clinically relevant findings in each image. Learn more: nejm.ai/44rk1a1 @dccastr0.bsky.social #AI #MedSky #MLSky

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Daniel Coelho de Castro @dccastr0.bsky.social · 26/06/2025

📣 Our new PadChest-GR benchmark in collaboration with @ua.es and MedBravo is published in @ai.nejm.org: ai.nejm.org/doi/full/10....! 📰 Also check out our blog post for why we're so excited about it: www.microsoft.com/en-us/resear... 💾 The dataset can be downloaded here: bimcv.cipf.es/bimcv-projec...

ai.nejm.org

PadChest-GR: A Bilingual Chest X-Ray Dataset for Grounded Radiology Report Generation

Artificial intelligence (AI)–powered radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Grounded radiology report generation (GRRG) augments RRG by ...

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