Multi-modal and multi-model approaches for medical image analysis: with applications to tumor and cancer detection

Advancements in medical imaging and artificial intelligence have revolutionized the field of tumor and cancer detection. However, single-modality imaging and conventional analysis models often face limitations in capturing complex pathological features. This seminar explores cutting-edge multi-modal and multi-model approaches for medical image analysis, leveraging diverse imaging techniques—such as MRI, CT, PET, and histopathology—along with deep learning and machine learning models to enhance diagnostic accuracy. By integrating complementary information from different imaging modalities and fusing insights from multiple AI-driven models, these approaches aim to improve early detection, segmentation, and classification of tumors. We will discuss key challenges, methodologies, and real-world applications, highlighting how multi-modal fusion and ensemble learning contribute to more robust and precise cancer diagnosis. This seminar is designed for researchers, clinicians, and AI practitioners interested in the intersection of medical imaging and computational intelligence for enhanced healthcare solutions.