Results 41 to 50 of about 5,768,655 (280)

U-Net for leaf vein segmentation.

, 2023
Images of the high-quality dataset (A) were converted to grayscale images and masked vein images were generated by conventional image processing with contrast enhancement and binarization (B) to prepare a training dataset.
Kohei Iwamasa (16624443), Koji Noshita (3326502)   +1 more
core   +1 more source

Comparison of our U-Net segmentation models with U-net models trained on the entire wings.

, 2023
Comparison of our U-Net segmentation models with U-net models trained on the entire wings.
Margarida Silveira (14608576), Carolina Cunha (6566972), Hemaxi Narotamo (5832848), Antónia Monteiro (63684)   +3 more
core   +1 more source

Relation U-Net

2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)
ISIB ...
Sheng He, Rina Bao, Patricia Ellen Grant, Yangming Ou   +3 more
openaire   +3 more sources

U-net weights for brain segmentation

, 2018
U-net weights for U ...
Brian Avants (418551), Nick Tustison (441144)   +1 more
core   +1 more source

U-net weights for denoising

, 2019
U-net weights for ...
Brian Avants (418551), Nick Tustison (441144)   +1 more
core   +1 more source

RS-U-Net.

, 2023
Reverberation is the primary background interference of active sonar systems in shallow water environments, affecting target position detection accuracy.
Zhen Wang (72451), Xiao Chen (260187), Yuan An (177578), Hao Zhang (15339)   +3 more
core   +1 more source

An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net

, 2021
Nuclei segmentation is an important step in the task of medical image analysis. Nowadays, deep learning techniques based on Convolutional Neural Networks (CNNs) have become prevalent methods in nuclei segmentation.
Wenting Duan (17157046), Qing Xu (17924819)   +1 more
core   +3 more sources

Agreement ratios between renal pathologists with and without U-Net-segmented images.

, 2022
Agreement ratios between renal pathologists with and without U-Net-segmented images.
Satoshi Hara (539722), Mitsuhiro Kawano (739143), Ryo Nishioka (10498823), Takashi Yoneda (4258531), Emi Haneda (13028930), Mitsuhiro Kometani (4258516), Shigehiro Karashima (4258534), Takeshi Zoshima (9939141), Masaki Kawakami (491164), Hidetaka Nambo (9330974), Kento Morita (13028933)   +10 more
core   +1 more source

Segmentation of skin cancer using Fuzzy U-network via deep learning

Measurement: Sensors, 2023
The most common cancer in the world is skin cancer. In recent years, one of the most important challenges to public health has been melanoma, the most dangerous type of skin cancer. In this paper, a novel MFO-Fuzzy U net has been proposed to segmentation
A. Bindhu, K.K. Thanammal
doaj   +1 more source

Kidney Segmentation of Histopathological Images with Edge-Aware U-Net to Support Medical Diagnosis and Treatment Planning

Bioengineering
Accurate segmentation of renal anatomical structures is essential for informed clinical decision-making in nephropathology, supporting precise diagnosis, treatment planning, and longitudinal monitoring of kidney diseases. In this work, we propose an Edge-
Esraa Hassan, Amira Samy Talaat, Shaimaa M. Hassan, Sameer Alqassimi, M. A. Elsabagh   +4 more
doaj   +1 more source