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Combining AI and X-ray physics to overcome tomography data gaps

With PFITRE, Brookhaven scientists achieve breakthrough 3D imaging in nanoscale X-ray tomography, combining AI and physics ...
Tech Xplore

Novel AI method sharpens 3D X-ray vision

X-ray tomography is a powerful tool that enables scientists and engineers to peer inside of objects in 3D, including computer ...
IEEE

U-Net V2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

In this paper, we introduce U-Net v2, a new robust and efficient U-Net variant for medical image segmentation. It aims to augment the infusion of semantic information into low-level features while ...
GitHub

Lanenet-Lane-Detection (pytorch version)

Use pytorch to implement a Deep Neural Network for real time lane detection mainly based on the IEEE IV conference paper "Towards End-to-End Lane Detection: an ...
Scientific Research Publishing

From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning ()

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
Frontiers

An improved U-net and attention mechanism-based model for sugar beet and weed segmentation

Introduction: Weeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision ...
Frontiers

MSGU-Net: a lightweight multi-scale ghost U-Net for image segmentation

U-Net and its variants have been widely used in the field of image segmentation. In this paper, a lightweight multi-scale Ghost U-Net (MSGU-Net) network architecture is proposed. This can efficiently ...
Scientific Research Publishing

From Traditional Methods to 3D U-Net: A Comprehensive Review of Brain Tumour Segmentation Techniques ()

Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating ...
IEEE

Motion U-Net: Multi-cue Encoder-Decoder Network for Motion Segmentation

Abstract: Detection of moving objects is a critical component of many computer vision tasks. Recently, deep learning architectures have been developed for supervised learning based moving object ...