Advancing Histopathology-Based Breast Cancer Diagnosis: Insights into Multi-Modality and Explainability

As a leading cause of mortality among women globally, the precise and timely diagnosis of breast cancer remains imperative for optimizing patient outcomes. While traditional diagnostic methodologies [2] have historically relied heavily on uni-modal approaches, the evolving landscape of medical data analytics underscores the significance of integrating diverse data sources beyond conventional imaging modalities [3]. Figure 1 illustrates a generic model for breast cancer diagnosis within the Computer-Aided Detection (CAD) framework. As depicted in Figure 2, breast cancer detection can be performed using various data types, employing either unimodal or multimodal approaches. The process initiates with data pre-processing, followed by feature extraction. To enhance the learning of feature representations from image data, segmentation may be conducted prior to feature extraction. Subsequently, the detection model is applied to generate a diagnosis from the processed data. Based on this diagnosis, further analyses are performed, including sub-type classification, grade classification, recurrence and metastasis prediction, as well as the incorporation of crowdsourcing and human-in-the-loop methodologies. These steps culminate in a final decision that informs subsequent treatment and monitoring strategies.