<p>Ovarian cancer remains one of the most challenging cancers to diagnose due to its non-specific symptoms, lack of reliable screening tests, and the complexity of detecting abnormalities. Accurate subtype classification is crucial for personalised treatment and improved patient outcomes. In this study, we developed a machine learning pipeline fine-tuning pre-trained computer vision models to classify ovarian cancer subtypes from whole slide images (WSI). Using targeted tissue masks for necrosis, stroma, and tumour regions as a proof of concept, we demonstrated the efficacy of tiling masked regions to transform a complex detection-then-classification problem into a simpler classification task. Our method achieved high accuracy in tile-level classification, with a subsequent extension to subtype classification via majority voting on tiled images. Precision exceeds 90% across subtypes, which highlights the potential of scalable, automated systems to assist in ovarian cancer diagnostics. These findings contribute to the broader field of computational pathology, paving the way for enhanced diagnostic consistency and accessibility in clinical settings.</p>