Medical Image
Medical image analysis focuses on extracting meaningful information from various imaging modalities (e.g., CT, MRI, X-ray) to improve diagnosis and treatment planning. Current research emphasizes developing robust and efficient algorithms, often employing convolutional neural networks (CNNs), transformers, and diffusion models, to address challenges like data variability, limited annotations, and privacy concerns. These advancements are crucial for improving the accuracy and speed of medical image analysis, leading to better patient care and accelerating medical research.
Papers
Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder
Manisha Das, Deep Gupta, Petia Radeva, Ashwini M Bakde
A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention
Payal Wankhede, Manisha Das, Deep Gupta, Petia Radeva, Ashwini M Bakde
Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images
Che Liu, Anand Shah, Wenjia Bai, Rossella Arcucci
Efficient Retrieval of Images with Irregular Patterns using Morphological Image Analysis: Applications to Industrial and Healthcare datasets
Jiajun Zhang, Georgina Cosma, Sarah Bugby, Jason Watkins