Results 221 to 230 of about 79,319 (254)

Diffusional magnetic resonance imaging anonymizing with variational autoencoder

Quantitative Biology, Volume 14, Issue 2, June 2026.
Abstract Anonymization is a crucial de‐identification technique that protects data privacy while ensuring its utility for model building. Current generative models such as generative adversarial networks and variational auto‐encoders (VAEs) have been applied to medical image anonymization but mainly focus on general image features, lacking specificity ...
Yunheng Shen, Ling Zheng, Ruohan Liu, Haoran Feng, Hairong Lv   +4 more
wiley   +1 more source

Jointly Learned 3D Non‐Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI

Magnetic Resonance in Medicine, Volume 95, Issue 5, Page 2554-2567, May 2026.
ABSTRACT Purpose To develop accelerated 3D phase contrast (PC) MRI using jointly learned wave encoding and reconstruction. Methods Pseudo‐fully sampled neurovascular 4D flow data (N = 40) and a simulation framework were used to learn phase encoding locations, wave readout parameters, and model‐based reconstruction network (MoDL) for a rapid 3D PC scan (
Chenwei Tang, Brock W. Jolicoeur, James Rice, Caroline A. Doctor, Zaynab S. Yardim, Leonardo A. Rivera‐Rivera, Laura B. Eisenmenger, Kevin M. Johnson   +7 more
wiley   +1 more source

Accelerating Multiparametric Quantitative MRI Using Self‐Supervised Scan‐Specific Implicit Neural Representation With Model Reinforcement

Magnetic Resonance in Medicine, Volume 95, Issue 5, Page 2963-2979, May 2026.
ABSTRACT Purpose To develop a self‐supervised scan‐specific deep learning framework for reconstructing accelerated multiparametric quantitative MRI (qMRI). Methods We propose REFINE‐MORE (REference‐Free Implicit NEural representation with MOdel REinforcement), combining an implicit neural representation (INR) architecture with a model reinforcement ...
Ruimin Feng, Albert Jang, Xingxin He, Fang Liu   +3 more
wiley   +1 more source

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Matting Enhanced Mask R-CNN

2021 IEEE International Conference on Multimedia and Expo (ICME), 2021
We propose a novel and effective method for high-quality instance segmentation. Top-performing "detect-then-segment" approaches (e.g., Mask R-CNN) rely on region-of-interest (ROI) cropping operations to obtain the final masks, but their performance is restricted by blurry boundary and average loss weight.
Lufan Ma, Bin Dong, Jiangpeng Yan, Xiu Li   +3 more
openaire   +1 more source

SE-Mask R-CNN: An improved Mask R-CNN for apple detection and segmentation

Journal of Intelligent & Fuzzy Systems, 2021
Fruit detection and segmentation is an essential operation of orchard yield estimation, the result of yield estimation directly depends on the speed and accuracy of detection and segmentation. In this work, we propose an effective method based on Mask R-CNN to detect and segment apples under complex environment of orchard.
Liu, Yikun, Yang, Gongping, Huang, Yuwen, Yin, Yilong   +3 more
openaire   +1 more source

MA Mask R-CNN: MPR and AFPN Based Mask R-CNN

2021
Multi-resolution parallel ResNet (MPR) and Attention FPN (AFPN) are presented based Mask R-CNN (MA Mask R-CNN) to achieve image instance segmentation, which aims at improving the feature extraction ability of the model, and efficiently increasing the detection and segmentation accuracy. MPR adds parallel branches to extract the feature before each down-
Sumin Qi, Shihao Jiang, Zhenwei Zhang
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MaskS R-CNN Text Detector

2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), 2020
Scene text detection and scene text recognition are important components of scene text recognition system. Scene text detection, the initial stage of scene text recognition, aims to find out text area in the picture. Recently the target detection method Mask R-CNN has been employed scene text detection and achieved good performance.
Pengfei Duan, Jiahao Pan, Wenbi Rao
openaire   +1 more source

Nuclei R-CNN: Improve Mask R-CNN for Nuclei Segmentation

2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP), 2019
Accurate nuclei segmentation plays an essential role in medical research and various clinical applications. Recently, deep learning has demonstrated its superior performance on object segmentation in natural scene images. However, these methods cannot produce fine segmentation in histopathological images.
Guofeng Lv, Ke Wen, Zheng Wu, Xu Jin, Hong An, Jie He   +5 more
openaire   +1 more source

Mask R-CNN Algoritması ile Hangar Tespiti Hangar Detection with Mask R-CNN Algorithm

2019 27th Signal Processing and Communications Applications Conference (SIU), 2019
In this study, the detection of hangars in high resolution airport (civil and military) satellite images was performed using Mask R-CNN algorithm. Although the detection of buildings in the satellite images is a common practice, being some of the hangars camouflaged in different sizes cause difficulty for the detection algorithms.
Aslı Nur Ömeroğlu, Nida Kumbasar, Emin Argun Oral, I. Yucel Ozbek   +3 more
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Mask R-CNN for Ear Detection

2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2019
Ear detection is an important step in ear recognition pipeline as it makes or breaks the system. However, in the literature there is arguably the lack of ear detection approaches available. This poses a problem for opening ear recognition system to wider use and applications in commercial systems. To tackle this problem we present the use of Mask R-CNN
Matic Bizjak, Peter Peer, Žiga Emeršič   +2 more
openaire   +1 more source