Multi-Task Learning for Bone X-Rays

The Publication

[Peer-Reviewed] This work was accepted into the International Bioimaging Conference (BIOIMAGING 2026), part of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) under INSTICC.

Official publication: Using Multi-Task Learning and CAM-Based Ensembles for Detection of Abnormalities in X-Rays, pages 350–358, ISBN 978-989-758-802-0.

The Challenge

Musculoskeletal abnormalities are a leading cause of global disability, yet manual radiological analysis suffers from high inter-observer variability and high workload. Detectable markers are often subtle, requiring an expert eye and a transparent AI assistant that clinicians can trust.

The Approach

We introduced a high-performance deep learning framework leveraging Multi-Task Learning (MTL) and Class Activation Map (CAM) based ensembles. By training on the MURA dataset to simultaneously identify body parts and detect abnormalities, we “forced” the feature extractors to learn robust, medically relevant representations.

[!IMPORTANT] Our novel ensemble method uses inferential weighting based on CAM localization focus—effectively giving more “vote” to models that pinpoint anomalies with higher spatial confidence.

The Result

Our MTL-enhanced models achieved a 6.0% improvement in Cohen’s Kappa over baseline single-task models. The final ensemble further boosted performance by 4.6%, proving that “smarter” averaging is superior to simple majority voting. The project includes an interactive dashboard to visualize these results in real-time.