Results 51 to 60 of about 6,547,057 (287)

Clinically Interpretable Nuclei Segmentation for Robust Histopathological Image Analysis

Applied Sciences
Background/Objectives: Accurate nuclear segmentation is a fundamental step in computational pathology, enabling reliable estimation of cellularity and nuclear morphology. However, segmentation models are typically evaluated under ideal imaging conditions,
Liana Stanescu, Cosmin Stoica Spahiu
doaj   +1 more source

U-Net-Based Models towards Optimal MR Brain Image Segmentation

Diagnostics, 2023
Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed.
Rammah Yousef, Shakir Khan, Gaurav Gupta, Tamanna Siddiqui, Bader M. Albahlal, Saad Abdullah Alajlan, Mohd Anul Haq   +6 more
doaj   +1 more source

DXM‐TransFuse U-net: Dual cross-modal transformer fusion U-net for automated nerve identification

Computerized Medical Imaging and Graphics, 2022
Accurate nerve identification is critical during surgical procedures for preventing any damages to nerve tissues. Nerve injuries can lead to long-term detrimental effects for patients as well as financial overburdens. In this study, we develop a deep-learning network framework using the U-Net architecture with a Transformer block based fusion module at
Xie, Baijun, Milam, Gary, Ning, Bo, Cha, Jaepyeong, Park, Chung Hyuk   +4 more
openaire   +3 more sources

Improving singing voice separation using Deep U-Net and Wave-U-Net with data augmentation

, 2019
State-of-the-art singing voice separation is based on deep learning making use of CNN structures with skip connections (like U-net model, Wave-U-Net model, or MSDENSELSTM).
bittner, bogaards, camacho, chandna, jansson, kingma, mcfee, rafii, ronneberger, röbel, röbel, röbel, schlüter, stoller, stöter, uhlich, vembu   +16 more
core   +3 more sources

TransClaw U-Net: Claw U-Net with Transformers for Medical Image Segmentation

, 2021
8 page, 3 ...
Chang, Yao, Menghan, Hu, Guangtao, Zhai, Xiao-Ping, Zhang   +3 more
openaire   +2 more sources

Is the U-NET Directional-Relationship Aware?

2022 IEEE International Conference on Image Processing (ICIP), 2022
Accepted at ICIP ...
Riva, Mateus, Gori, Pietro, Yger, Florian, Bloch, Isabelle   +3 more
openaire   +3 more sources

Fully Convolutional Networks for Automated Segmentation of Abdominal Adipose Tissue Depots in Multicenter Water-Fat MRI

, 2018
Purpose: An approach for the automated segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in multicenter water-fat MRI scans of the abdomen was investigated, using two different neural network architectures.
Ahlström, Håkan, Bergsten, Peter, Forslund, Anders, Hedström, Anders, Kullberg, Joel, Langner, Taro, Mörwald, Katharina, Weghuber, Daniel   +7 more
core   +1 more source

Comparison of Three Convolution Neural Network Schemes to Retrieve Temperature and Humidity Profiles from the FY4A GIIRS Observations

Remote Sensing, 2022
FY4A/GIIRS (Geostationary Interferometric Infrared Sounder) is the first infrared hyperspectral atmospheric vertical sounder onboard a geostationary satellite.
Shuhan Yao, Li Guan
doaj   +1 more source

Patient Activation in Childhood, Adolescent, and Young Adult Cancer Survivors: Current Insights and Implications for Survivorship Care—A Systematic Review From the e‐QuoL Project

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Purpose Patient activation—encompassing knowledge, confidence, and skills in managing individual's health—is a cornerstone of person‐centered care. However, its significance among childhood, adolescent, and young adult cancer survivors (CAYACS) remains unexplored. This article examines the application of the 13‐item Patient Activation Measure (
Charlotte Demoor‐Goldschmidt, Anne Maas, Gisela Michel, Amandine Bertrand, Kristen E. T. Thornton, Martine Bellanger, Anna‐Liesa Filbert, Desiree Grabow, Monica Muraca, Katharina Roser, Sara Oberti, Jelena Roganovic, Hanne Cathrine Lie   +12 more
wiley   +1 more source

Projection-Based 2.5D U-net Architecture for Fast Volumetric Segmentation

, 2019
Convolutional neural networks are state-of-the-art for various segmentation tasks. While for 2D images these networks are also computationally efficient, 3D convolutions have huge storage requirements and require long training time.
Angermann, Christoph, Gizewski, Elke, Haltmeier, Markus, Pereverzyev Jr, Sergiy, Steiger, Ruth   +4 more
core   +1 more source