IEEE Transactions on Medical Imaging
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#1Yinghuan Shi (NU: Nanjing University)H-Index: 18
#2Jian Zhang (NU: Nanjing University)H-Index: 2
Last. Yang Gao (NU: Nanjing University)H-Index: 53
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In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conser...
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#1Yan WangH-Index: 15
#2Zizhou WangH-Index: 2
Last. Lei ZhangH-Index: 145
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The early detection and timely treatment of breast cancer can save lives. Mammography is one of the most efficient approaches to screening early breast cancer. An automatic mammographic image classification method could improve the work efficiency of radiologists. Current deep learning-based methods typically use the traditional softmax loss to optimize the feature extraction part, which aims to learn the features of mammographic images. However, previous studies have shown that the feature extr...
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#1Oz FrankH-Index: 1
#2Nir SchipperH-Index: 1
Last. Libertario DemiH-Index: 20
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Lung ultrasound (LUS) is a cheap, safe and non-invasive imaging modality that can be performed at patient bed-side. However, to date LUS is not widely adopted due to lack of trained personnel required for interpreting the acquired LUS frames. In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS. When using LUS to evaluate a patient's condition, both anatomical phenomena (e.g., the pleural line, presence of con...
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#1Jiawei Chen (Tencent)H-Index: 9
#2Ziqi Zhang (THU: Tsinghua University)
Last. Yefeng Zheng (Tencent)H-Index: 38
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Medical images from multicentres often suffer from the domain shift problem, which makes the deep learning models trained on one domain usually fail to generalize well to another. One of the potential solutions for the problem is the generative adversarial network (GAN), which has the capacity to translate images between different domains. Nevertheless, the existing GAN-based approaches are prone to fail at preserving image-objects in image-to-image (I2I) translation, which reduces their practic...
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#1Fatmatulzehra Uslu (Bursa Technical University)H-Index: 1
#2Marta Varela (NIH: National Institutes of Health)H-Index: 13
Last. Anil A. Bharath (Imperial College London)H-Index: 21
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Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI. LA-Net includes cross atten...
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#1Nima TajbakhshH-Index: 18
#2Holger R. RothH-Index: 29
Last. Jianming LiangH-Index: 30
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With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility treatment, in which only the embryos that were transferred to the female uterus may have labels of pregnancy. In this paper, we apply a self-supervised video alignment method known as temporal cycle-consistency (TCC) on 38;176 time-lapse videos of develo...
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#1Juhyung Park (SNU: Seoul National University)
#2Woojin Jung (SNU: Seoul National University)H-Index: 6
Last. Jongho Lee (SNU: Seoul National University)H-Index: 22
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In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-value that are the same as the training data. In this study, a new deep neural network, referred to as DIFFnet, is developed to function as a generalized reconstruction tool of the diffusion-weighted signals for various gradient schemes and b-values. ...
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#1Malte Hoffmann (Harvard University)H-Index: 2
#2Benjamin Billot (UCL: University College London)H-Index: 6
Last. Adrian V. Dalca (Harvard University)H-Index: 22
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We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during trainin...
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#1Zhenyuan Ning (Southern Medical University)H-Index: 5
#2Shengzhou Zhong (Southern Medical University)
Last. Yu Zhang (Southern Medical University)H-Index: 21
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Deep learning methods, especially convolutional neural networks, have been successfully applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern complexity and intensity similarity between the surrounding tissues (i.e., background) and lesion regions (i.e., foreground) bring challenges for lesion segmentation. Considering that such rich texture information is contained in background, very few methods have tried to explore and exploit background-salient representations f...
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Image segmentation
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Pattern recognition
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