Multi Branch Hovernet Framework

Multi-branch HoverNet frameworks are primarily used for instance segmentation and classification tasks, particularly in biomedical image analysis, focusing on identifying and categorizing objects like nuclei in microscopy images or lesions in ultrasound scans. Current research emphasizes improving efficiency and accuracy through architectural modifications, such as integrating UNet structures and Vision Transformers, and exploring variations like MF-HoverNet to enhance performance in specific applications. These advancements offer improved speed and accuracy in automated image analysis, with significant implications for medical diagnostics and other fields requiring precise object detection and classification within complex images.