Medical Imaging

Machine learning techniques have emerged as a valuable tool for improving medical imaging problems, offering promising solutions to enhance diagnosis and treatment. In the context of knee disorder diagnosis from MRI images, few-shot learning has gained attention due to the limited availability of labeled data. By utilizing a few-shot learning approach with fuzzy layers, the model can effectively learn from a small set of examples and adapt its knowledge to accurately diagnose knee disorders. This approach allows medical professionals to make informed decisions based on a broader range of cases, leading to improved accuracy and efficiency in diagnosing and treating knee disorders.

Additionally, convolutional neural networks (CNNs) have shown great potential in the fusion of photoacoustic images. Photoacoustic imaging combines ultrasound and laser-induced optical contrast to provide detailed and functional information about tissues. By leveraging CNNs, these multimodal images can be effectively fused, allowing for a comprehensive and integrated analysis of anatomical and functional features. The CNN-based fusion process enables the extraction of relevant features from each modality, preserving the valuable information and enhancing the overall image quality and interpretation. This fusion approach in photoacoustic imaging holds significant promise in various medical applications, such as cancer detection, tissue characterization, and vascular imaging, ultimately leading to improved diagnostic accuracy and patient care.

Reference

1. M Singh et al., A Few-shot approach to MRI-based Knee Disorder Diagnosis using Fuzzy Layers, ICVGIP 2022.
2. N Awasthi et al., PA-Fuse: A Deep Supervised Approach for Fusion of Photoacoustic Images with Distinct Reconstruction Characteristics, JOSA BOE 2019.