Knee Osteoarthritis Diagnosis Integrating Meta-Learning and Multi-task Convolutional Neural Network

Applications of deep learning, in particular Convolutional Neural Networks (CNNs), have shown promise in computer-aided diagnosis, including analysis of osteoarthritis in the knee. Focusing on two of the most popular tasks in medical imaging - segmentation and classification - this work investigates the novelty of adding meta-learning to the multitask learning (MTL) technique for volumetric analysis employing Magnetic Resonance Imaging (MRI) data in the diagnosis of knee osteoarthritis. To enhance the performance of each task, we incorporate recent advances in meta-learning, specifically Model-Agnostic Meta-Learning (MAML) and MetaNet. Experimental results indicate that the innovative integration of meta-learning performs better than all other models. Specifically, MAML compensated for the limited segmentation enhancement seen in the MTL model alone, demonstrating better overall performance. These findings demonstrate MAML's remarkable capacity to handle challenging multi-task medical image analysis, successfully striking a balance between segmentation and classification accuracy. With the ability to concurrently execute osteoarthritis classification and knee structure segmentation in 3D MRI, this work addresses the computational problems associated with 3D medical imaging and advances the effectiveness of diagnostic models in the field.