Cancer Detection

Cancer detection research intensely focuses on improving the accuracy and efficiency of diagnosis using various imaging modalities (e.g., ultrasound, mammograms, CT scans) and genomic data. Current efforts leverage deep learning architectures, including convolutional neural networks (CNNs) and transformers, often combined with techniques like transfer learning, semi-supervised learning, and multiple instance learning to address challenges such as limited annotated data and inter-patient variability. These advancements aim to enable earlier, more accurate cancer detection, ultimately improving patient outcomes and streamlining clinical workflows. The field is also exploring the integration of multimodal data and explainable AI to enhance both diagnostic performance and clinical interpretability.