is essential in medical image segmentation tasks because 1) the scale of medical image dataset is typically smaller, which may increase the risk of overfitting; 2) the shape and modality of different objects such as organs or tumors are unique, thus requiring customized data augmentation policy. ...
【论文翻译】Data augmentation using learned transforms for one-shot medical image segmentation 论文原文:https://arxiv.org/abs/1902.09383 完整的图、表及引用见原文,用于学习记录,与有需要的人分享。 根据图理解方法 第一步,将有标记的x和无标记的y通过一个CNN网络的学习,得到一个空间转换模型,即学习到一个...
综述了脑肿瘤磁共振图像数据增强(data augmentation)技术的最新进展。 (BraTS 2018)数据集特征:Also, it is heterogeneous in the sense that it includes both low- and highgrade lesions, and the included MRI scans have been acquired at differentinstitutions(using different MR scanners). 它是异质性的,因...
In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common ...
Data augmentation using learned transformsfor one-shot medical image segmentation (cvpr 2019) arxiv: arxiv.org/pdf/1902.0938 github: github.com/xamyzhao/bra. This paper propose a data augmentation method for medical images. Only one annotated image (x, l) is used as reference atlas and other...
For image segmentation model, U-net28is the most commonly used supervised learning method in the field of materials and medical image processing29. Therefore, we used U-net as our baseline to compare with data augmentation algorithms. U-net is an encoder–decoder network; the input goes through...
采用[12]中引入的图像-图像转换条件GAN(pix2pix)模式,对标签- mri (syntheticimagegeneration)和mri -标签(image segmentation)进行转换。对于脑分割,生成器G给出一个t1加权的ADNI图像作为输入,训练生成一个含有白质、灰质和脑脊液的脑掩膜。在这个过程中(如图1所示),产生器G学会了从t1加权的MRI输入中分割大脑标签...
we introduce the first diffusion-based augmentation method for nuclei segmentation. The idea is to synthesize a large number of labeled images to facilitate training the segmentation model. To achieve this, we propose a two-step strategy. In the first step, we train an unconditional diffusion mode...
data augmentation, image recognition References HU T, QI H, HUANG Q, et al. See better beforelooking closer: Weakly supervised data augmentation network for fine-grained visual categorization[J]. arXiv preprint arXiv, 2019 1901: 9891. RAO Y, CHEN G, LU J, et al. Counterfactual attention...
Data Augmentations for n-Dimensional Image Input to CNNsmlnotebook.github.io/post/dataaug/ One of the greatest limiting factors for training effective deep learning frameworks is the availability, quality and organisation of the training data. To be good at classification tasks, we need to ...