Results 21 to 30 of about 5,768,655 (280)

Attention-augmented U-Net (AA-U-Net) for semantic segmentation

Signal, Image and Video Processing, 2022
Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, training data are currently scarce. Attention models, in particular the most recent self-attention methods, have shown to
Kumar T. Rajamani, Priya Rani, Hanna Siebert, Elagiri Ramalingam Rajkumar, Mattias P. Heinrich   +4 more
openaire   +2 more sources

Perbandingan Model U-Net dan ELU-Net untuk Segmentasi Semantik Citra Medis Kanker Pankreas

Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Analisis citra medis untuk melakukan segmentasi semantik menggunakan teknologi deep learning masif dikembangkan saat ini. Salah satu pengembangannya adalah arsitektur U-Net, yang dapat menghasilkan akurasi yang baik. Pengembangan dilanjutkan menghasilkan
Algi Fari Ramdhani, Yudi Widhiyasana, Setiadi Rachmat   +2 more
doaj   +2 more sources

Segmentation of roots in soil with U-Net [PDF]

Plant Methods, 2020
Abstract Background Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods.
Abraham George Smith, Jens Petersen, Raghavendra Selvan, Camilla Ruø Rasmussen   +3 more
openaire   +7 more sources

Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography

Scientific Reports, 2022
Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies.
Kang Hsu, Da-Yo Yuh, Shao-Chieh Lin, Pin-Sian Lyu, Guan-Xin Pan, Yi-Chun Zhuang, Chia-Ching Chang, Hsu-Hsia Peng, Tung-Yang Lee, Cheng-Hsuan Juan, Cheng-En Juan, Yi-Jui Liu, Chun-Jung Juan   +12 more
doaj   +1 more source

Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach

Sensors, 2021
The automatic segmentation of the pancreatic cyst lesion (PCL) is essential for the automated diagnosis of pancreatic cyst lesions on endoscopic ultrasonography (EUS) images. In this study, we proposed a deep-learning approach for PCL segmentation on EUS
Seok Oh, Young-Jae Kim, Young-Taek Park, Kwang-Gi Kim   +3 more
doaj   +1 more source

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

IEEE Access, 2021
U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for ...
Nahian Siddique, Sidike Paheding, Colin P. Elkin, Vijay Devabhaktuni   +3 more
doaj   +1 more source

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

CoRR, 2021
8 page, 3 ...
Yao Chang, Menghan Hu, Guangtao Zhai, Xiao-Ping Zhang 0002   +3 more
openaire   +3 more sources

Multiple U-Net-Based Automatic Segmentations and Radiomics Feature Stability on Ultrasound Images for Patients With Ovarian Cancer

Frontiers in Oncology, 2021
Few studies have reported the reproducibility and stability of ultrasound (US) images based radiomics features obtained from automatic segmentation in oncology. The purpose of this study is to study the accuracy of automatic segmentation algorithms based
Juebin Jin, Haiyan Zhu, Haiyan Zhu, Jindi Zhang, Yao Ai, Ji Zhang, Yinyan Teng, Congying Xie, Congying Xie, Xiance Jin   +9 more
doaj   +1 more source

U-Net epoch-loss curves.

, 2022
U-Net epoch-loss curves.
Guoxiong Zhou (12245608), Chuang Cai (3471272), Liujun Li (12245611), Tao Liu (10785), Liangji Zhang (11128280), Weiwei Cai (1714009)   +5 more
core   +1 more source

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   +4 more sources