Abstract: Existing studies of multi-modality medical image segmentation tend to aggregate all modalities without discrimination and employ multiple symmetric encoders or decoders for feature ...

Overview OpenCV courses on Coursera provide hands-on, career-ready skills for real-world computer vision ...

This is the MATLAB code for the implementation of neural pupil engineering FPM (NePE-FPM), an optimization framework for FPM reconstruction for off-axis areas. NePE-FPM engineers the pupil function ...

Abstract: Semi-supervised learning based on consistency learning offers significant promise for enhancing medical image segmentation. Current approaches use copy-paste as an effective data ...

In recent years, the rapid development of machine vision based on artificial intelligence (AI) has gained increasing attention in agriculture (Abbasi et al., 2022; Maraveas, 2024). This becomes ...

Marine scientists have been leveraging supervised machine learning algorithms to analyze image and video data for nearly two decades. There have been many advances, but the cost of generating expert ...

This work presents a valuable self-supervised method for the segmentation of 3D cells in microscopy images, alongside an implementation as a Napari plugin and an annotated dataset. While the Napari ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. We implemented a multimodal set of functional imaging techniques optimized for ...

Semantic segmentation of medical images holds significant potential for enhancing diagnostic and surgical procedures. Radiology specialists can benefit from automated segmentation tools that ...