Projects

UMIE (Unified Medical Imaging Environment) standardizes 882,774 images across 20+ open medical datasets covering CT, MRI, and X-ray modalities. Pipelines download, clean, and annotate each source, outputting .png assets with harmonized metadata, segmentation masks, and RadLex-compliant labels.

A new approach to domain adaptation called cross-adaptation, which enables machine learning models to generalize across datasets from different sources — without requiring labeled examples from the target domain.

training-free retrieval-augmented classification framework that can enhance any VLM with minimal resources.

RocketRAG is a speed-focused Retrieval-Augmented Generation framework from TheLion.ai.

In this study, we developed and evaluated machine learning models to predict catheter-induced coronary and aortic dissections (CID) based on clinical, anatomical, and procedural data from more than 80,000 catheterized patients.

The study investigated whether routine complete blood count (CBC) tests can be used to detect SARS-CoV-2 infection using machine learning (ML). The motivation was to create a cost-effective, scalable screening tool that could complement or preselect patients for RT-PCR testing.

LOMA (Local Offline Medical Assistant) delivers a zero-cloud experience: the entire assistant, from embeddings to language model responses, runs on the user’s phone.

Automatic parametrization model for Polish radiology reports using deep language models. Trained on 1,200 annotated chest CT reports labeled with 44 observation tags, our model achieves an F1 score of 81%.

Renal kidney tumours classification