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arXiv AI By Jayanie Bogahawatte, Sachith Seneviratne, Simon Maksour, Dzung Do-Ha, Elizabeth Qian, Samuel Brennan, Matthew Keon, Lezanne Ooi, Saman Halgamuge

Model-agnostic Knowledge Augmented Machine Learning for Weakly Annotated Bioimaging Data

Read the original on arXiv AI →

The paper introduces a model‑agnostic knowledge augmentation technique that lets machine learning models leverage domain knowledge beyond automated feature extraction for weakly annotated bioimaging data. This method improves classification across seven ML architectures, achieving the best performance when combined with masked autoencoder self‑supervised learning, with precision gains of 2.9% on three in‑house microscopy datasets and 6.8% on three public cytomorphology benchmarks. Applied to nucleus‑level classification of TDP‑43 mislocalization in motor neuron images, the approach uncovers ALS‑related biological insights, such as dose‑dependent effects of stressors and variable pathology in sporadic versus familial ALS donors.

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