A Unified Multi-Task Vision Transformer for Interpretable Ovarian Tumour Analysis

Ovarian cancer remains a leading cause of gynaecological cancer mortality, and ultrasound-based deep learning systems for its diagnosis are typically built as separate post hoc processes for classification, segmentation, and interpretability, which introduces workflow inefficiencies and may produce inconsistent predictions. This work addresses that limitation. We propose UM-TOTA (Unified Multi-Task Ovarian Tumour Architecture), a Vision Transformer (ViT)-based architecture that performs eight-class tumour classification, three-class malignancy detection, tumour segmentation, and clinical concept interpretability within a single unified framework. We integrate a concept bottleneck guided by the IOTA and O-RADS clinical guidelines to enable transparent decision-making through medical concepts that clinicians can understand, and we employ combined adaptive t-vMF Dice and boundary-enhanced segmentation losses with progressive task weighting to stabilise multi-task optimisation. We evaluated the model on the Multi-Modality Ovarian Tumor Ultrasound (MMOTU) 2D dataset under two protocols: image-level 5-fold stratified cross-validation, and the patient-disjoint partition released with the dataset. Under cross-validation, UM-TOTA achieved 80.26% ± 1.10% accuracy (97.06% one-vs-rest macro specificity) for eight-class classification, 90.88% ± 1.14% accuracy (90.41% specificity) for malignancy detection, and 77.29% ± 1.29% Dice for segmentation. Under the patient-disjoint partition, which excludes any overlap of patients between training and testing, the corresponding values were 78.46%, 89.13%, and 75.41%, a reduction of under 2.2 percentage points on every metric. The UM-TOTA reduced the computational parameter load by approximately 65.1% relative to sequential single-task pipelines. The learned concepts aligned with established malignancy criteria, identifying vascularisation, solid components, and papillary projections as key predictors. This unified approach offers an efficient and interpretable framework for clinical ovarian ultrasound workflows.