Ovarian cancer, particularly high-grade serous carcinoma, is highly lethal, and accurate survival prediction is essential for treatment planning. However, traditional prognostic models rely on limited clinical and histologic features, while deep learning approaches require expensive pixel-level annotations of whole-slide histopathology images, limiting scalability. We propose a weakly supervised attention-based multiple instance learning (MIL) framework that predicts ovarian cancer survival using only slide-level survival labels. Each whole-slide image is treated as a bag of patches, where a patch encoder extracts features using a pre-trained CNN or vision transformer. An attention-based MIL aggregator assigns importance weights to patches, and a survival head outputs a risk score via a deep Cox model. The attention mechanism enhances interpretability by identifying prognostically relevant regions such as aggressive tumor morphology, stromal patterns, and immune infiltration. This reduces the need for manual annotation while preserving clinical relevance. The framework provides a scalable and interpretable approach for survival prediction and can be evaluated on datasets such as TCGA-OV and CPTAC for clinical translation.
Clinical Intelligence Research Press