Weiyao Wang spent eight years at Meta — his first job out of college — helping build multimodal perception systems and contributing to open-world segmentation projects, including SAM3D. His final day ...

Learn how Intersection over Union (IoU) works and how to implement it step-by-step using PyTorch. This guide covers everything from the basic concept to practical coding examples for object detection ...

Abstract: We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity ...

This study presents a deep learning-based approach for the automated segmentation of corrosion damage in scanning electron microscopy (SEM) images. The proposed method enables rapid and accurate ...

Abstract: U-Nets have been established as a standard neural network architecture for image-to-image problems such as segmentation and inverse problems in imaging. For high-dimensional applications, as ...

Recap: It has been a busy month for the Raspberry Pi Foundation. Shortly after announcing branded SD cards and a silicon bumper case for the Pi 5, the foundation introduced a series of branded SSDs ...

Deep convolutional neural network (CNN) greatly promotes the automatic segmentation of medical images. However, due to the inherent properties of convolution operations, CNN usually cannot establish ...

Cells were stained by tartrate-resistant acid phosphatase (TRAP) staining (Sigma-Aldrich, St. Louis, MO, United States), and images were captured using the BZ-X810 inverted microscope (Keyence, Osaka, ...

We propose a novel multi-level dilated residual neural network, an extension of the classical U-Net architecture, for biomedical image segmentation. U-Net is the most popular deep neural architecture ...

Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, ...