Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLC

Researchers developed a distribution-based deep multiple instance learning method for tumor proportion scoring in non-small cell lung cancer, using only slide-level labels to predict zero-inflated beta parameters. The approach outperforms baseline regression models and improves prediction accuracy and explainability.

arXiv:2606.27579v1 Announce Type: new Abstract: Accurate assessment of tumor proportion score TPS in non-small cell lung cancer NSCLC is critical for treatment planning and prognosis. Key challenges include the tedious manual work required to annotate each slide, combined with the limited number of experts certified for this task. Multiple instance learning MIL has proven to be an effective approach for predicting TPS scores at the slide level; however, existing methods struggle with non-expressive zero class images. Our approach involves two models: 1 an embedding-extraction and multiclass-classification network that captures the histopathological features of individual patches, and 2 a MIL model that aggregates these embeddings to predict zero-inflated beta ZIBeta parameters representing the overall TPS probability distribution for the entire slide. Using only slide-level TPS scores as labels, we demonstrate how this end-to-end framework can leverage a novel distribution-based architecture to improve prediction accuracy and explainability. ZIBeta modeling significantly outperforms baseline linear and ridge regression while capturing expected accuracy through distribution concentration.