The field of medical imaging is witnessing a significant shift towards the integration of multimodal data, leveraging the strengths of different data sources to improve diagnostic accuracy and clinical decision-making. Recent developments have focused on designing innovative frameworks that can seamlessly combine diverse data modalities, such as ultrasound images, clinical records, and histopathology images, to enhance disease diagnosis and subtyping. Notably, the use of deep learning techniques has shown great promise in improving the performance of medical image analysis tasks, including breast cancer detection and fetal plane classification. The incorporation of clinically-inspired approaches, such as adaptive contrast adjustment and dual-based representation of whole slide images, has further boosted the effectiveness of these models.
Noteworthy papers include: The paper on Multimodal Deep Learning for Phyllodes Tumor Classification, which proposes a dual-branch neural network that integrates breast ultrasound images with structured clinical data to improve diagnostic accuracy. The paper on Adaptive Contrast Adjustment Module, which introduces a plug-and-play module that employs a shallow texture-sensitive network to predict clinically plausible contrast parameters, resulting in improved performance across diverse models. The paper on Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping, which presents a novel framework that seamlessly integrates data from various modalities to enhance breast cancer subtyping. The paper on Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection, which evaluates the application of machine learning and deep learning techniques for breast cancer classification using ultrasound images, achieving high accuracy and perfect sensitivity for malignant lesions. The paper on UOPSL: Unpaired OCT Predilection Sites Learning for Fundus Image Diagnosis Augmentation, which proposes a novel unpaired multimodal framework that utilizes extensive OCT-derived spatial priors to dynamically identify predilection sites, enhancing fundus image-based disease recognition.
