Enhancing and Adapting in the Clinic: Source-Free Unsupervised Domain Adaptation for Medical Image Enhancement [0.03%]
基于源无约束领域的医学图像增强技术
Heng Li,Ziqin Lin,Zhongxi Qiu et al.
Heng Li et al.
Medical imaging provides many valuable clues involving anatomical structure and pathological characteristics. However, image degradation is a common issue in clinical practice, which can adversely impact the observation and diagnosis by phy...
Efficient Supervised Pretraining of Swin-Transformer for Virtual Staining of Microscopy Images [0.03%]
高效的Swin变换器有监督预训练在显微图像虚拟染色中的应用
Jiabo Ma,Hao Chen
Jiabo Ma
Fluorescence staining is an important technique in life science for labeling cellular constituents. However, it also suffers from being time-consuming, having difficulty in simultaneous labeling, etc. Thus, virtual staining, which does not ...
Spach Transformer: Spatial and Channel-Wise Transformer Based on Local and Global Self-Attentions for PET Image Denoising [0.03%]
基于局部和全局自注意力的时空通道变换器PET图像去噪方法
Se-In Jang,Tinsu Pan,Ye Li et al.
Se-In Jang et al.
Position emission tomography (PET) is widely used in clinics and research due to its quantitative merits and high sensitivity, but suffers from low signal-to-noise ratio (SNR). Recently convolutional neural networks (CNNs) have been widely ...
SC-SSL: Self-Correcting Collaborative and Contrastive Co-Training Model for Semi-Supervised Medical Image Segmentation [0.03%]
基于自校正协作对比联合训练的半监督医学图像分割方法
Juzheng Miao,Si-Ping Zhou,Guang-Quan Zhou et al.
Juzheng Miao et al.
Image segmentation achieves significant improvements with deep neural networks at the premise of a large scale of labeled training data, which is laborious to assure in medical image tasks. Recently, semi-supervised learning (SSL) has shown...
Qingsong Yao,Zecheng He,Yuexiang Li et al.
Qingsong Yao et al.
Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discovered that conventional adversarial attacks like PGD which op...
Aditya Murali,Deepak Alapatt,Pietro Mascagni et al.
Aditya Murali et al.
Assessing the critical view of safety in laparoscopic cholecystectomy requires accurate identification and localization of key anatomical structures, reasoning about their geometric relationships to one another, and determining the quality ...
Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism [0.03%]
基于不确定性感知注意机制的肺结节分割与不确定区域预测
Han Yang,Qiuli Wang,Yue Zhang et al.
Han Yang et al.
Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as th...
Self-Supervised Deep Unrolled Reconstruction Using Regularization by Denoising [0.03%]
基于去噪正则化的自监督深度迭代重建方法
Peizhou Huang,Chaoyi Zhang,Xiaoliang Zhang et al.
Peizhou Huang et al.
Deep learning methods have been successfully used in various computer vision tasks. Inspired by that success, deep learning has been explored in magnetic resonance imaging (MRI) reconstruction. In particular, integrating deep learning and m...
Edge-Guided Contrastive Adaptation Network for Arteriovenous Nicking Classification Using Synthetic Data [0.03%]
基于合成数据的动脉静脉标记分类的边缘引导对比自适应网络
Jicheng Liu,Hui Liu,Huazhu Fu et al.
Jicheng Liu et al.
Retinal arteriovenous nicking (AVN) manifests as a reduced venular caliber of an arteriovenous crossing. AVNs are signs of many systemic, particularly cardiovascular diseases. Studies have shown that people with AVN are twice as likely to h...
Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET [0.03%]
辅助传输衰减校正的心脏定量PET扫描补充方案
Mi-Ae Park,Vlad G Zaha,Ramsey D Badawi et al.
Mi-Ae Park et al.
Quantitative PET attenuation correction (AC) for cardiac PET/CT and PET/MR is a challenging problem. We propose and evaluate an AC approach that uses coincidences from a relatively weak and physically fixed sparse external source, in combin...