Subject-Specific Automatic Reconstruction of White Matter Tracts [0.03%]
基于主题的大脑白质纤维束自动追踪重建方法研究进展及其应用评估
Stephan Meesters,Maud Landers,Geert-Jan Rutten et al.
Stephan Meesters et al.
MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of method...
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis [0.03%]
利用组织学图像改进乳腺癌诊断分类性能的降维深度卷积激活特征（R-DeCAF）
Bahareh Morovati,Reza Lashgari,Mojtaba Hajihasani et al.
Bahareh Morovati et al.
Breast cancer is the second most common cancer among women worldwide, and the diagnosis by pathologists is a time-consuming procedure and subjective. Computer-aided diagnosis frameworks are utilized to relieve pathologist workload by classi...
An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection [0.03%]
一种可解释的MRI-影像组学量子神经网络通过量子退火进行特征选择以区分大型脑转移瘤和高度恶性的胶质瘤
Tony Felefly,Camille Roukoz,Georges Fares et al.
Tony Felefly et al.
Solitary large brain metastases (LBM) and high-grade gliomas (HGG) are sometimes hard to differentiate on MRI. The management differs significantly between these two entities, and non-invasive methods that help differentiate between them ar...
ExpHBA Deep-IoT: Exponential Honey Badger Optimized Deep Learning For Breast Cancer Detection in IoT Healthcare System [0.03%]
基于指数型蜜獾优化的深度学习乳腺癌检测物联网医疗系统
R Rajeswari,G V Sriramakrishnan,Ch Vidyadhari et al.
R Rajeswari et al.
Breast cancer (BC) is the most widely found disease among women in the world. The early detection of BC can frequently lessen the mortality rate as well as progress the probability of providing proper treatment. Hence, this paper focuses on...
A Novel Classification Model Using Optimal Long Short-Term Memory for Classification of COVID-19 from CT Images [0.03%]
一种用于从CT图像中分类COVID-19的最优长短期记忆分类模型的新方法
R Vinothini,G Niranjana,Fitri Yakub
R Vinothini
The human respiratory system is affected when an individual is infected with COVID-19, which became a global pandemic in 2020 and affected millions of people worldwide. However, accurate diagnosis of COVID-19 can be challenging due to small...
ECTransNet: An Automatic Polyp Segmentation Network Based on Multi-scale Edge Complementary [0.03%]
基于多尺度边缘互补的自动息肉分割网络ECTransNet
Weikang Liu,Zhigang Li,Chunyang Li et al.
Weikang Liu et al.
Colonoscopy is acknowledged as the foremost technique for detecting polyps and facilitating early screening and prevention of colorectal cancer. In clinical settings, the segmentation of polyps from colonoscopy images holds paramount import...
Initial Experience of 10 Imaging Vendors with the IHE SHARAZONE: a New Multivendor Peer-to-Peer Test Service for DICOM Objects [0.03%]
10家影像设备商首次使用IHE SHARAZONE的情况汇报：一种新的多厂商对等测试服务用于DICOM对象
Steven Nichols,Bruno Laffin,Charles Parisot
Steven Nichols
Alignment of DICOM (Digital Imaging and Communications in Medicine) capabilities among vendors is crucial to improve interoperability in the healthcare industry and advance medical imaging 2. However, a sustainable model for sharing DICOM s...
Correction to: Lossy Image Compression in a Preclinical Multimodal Imaging Study [0.03%]
对一项临床前多模态成像研究中的有损图像压缩的改正
Francisco F Cunha,Valentin Blüml,Lydia M Zopf et al.
Francisco F Cunha et al.
Published Erratum
Journal of digital imaging. 2023 Oct;36(5):2322. DOI:10.1007/s10278-023-00879-w 2023
Artificial Intelligence Techniques for Automatic Detection of Peri-implant Marginal Bone Remodeling in Intraoral Radiographs [0.03%]
基于口腔曲面体层摄影图像的种植体周围骨缺损的人工智能检测技术
María Vera,María José Gómez-Silva,Vicente Vera et al.
María Vera et al.
Peri-implantitis can cause marginal bone remodeling around implants. The aim is to develop an automatic image processing approach based on two artificial intelligence (AI) techniques in intraoral (periapical and bitewing) radiographs to ass...
Residual Deformable Split Channel and Spatial U-Net for Automated Liver and Liver Tumour Segmentation [0.03%]
基于残差可变形分隔通道和空间U形网络的肝部分割及肝肿瘤自动化分割方法
S Saumiya,S Wilfred Franklin
S Saumiya
Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net for liver and LT segmentation achieves better results than manual segmentation, it loses sp...