Breast Cancer Diagnosis

Breast cancer diagnosis research focuses on improving accuracy and efficiency through advanced computational methods, primarily leveraging machine learning and deep learning models. Current efforts concentrate on integrating multi-modal data (e.g., mammograms, ultrasound, histopathology), employing architectures like convolutional neural networks (CNNs) and transformers, and enhancing model interpretability (Explainable AI or XAI) for increased clinician trust and improved patient care. These advancements aim to improve early detection, leading to better treatment outcomes and potentially reducing mortality rates, while also addressing challenges like data scarcity and model robustness.