【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...
问题:解决one-shot医学图像分割 方案: 传统的基于Atlas的医学图像分割对于解决one-shot问题很有帮助,通过使用神经网络进行替代传统纹理计算的方式计算一致性 引入强化学习,充分利用训练数据 结论: 思路方法很好,通过方法的结合进行提出新的方案是一个很好的思路。 Atlas-based segmentation: 基于图谱的图像分割 Atlas:指人...
为了解决缺少标注的问题,借助经典的atlas-based方法的分割思想来解决one-shot分割问题 以端到端方式将对应映射关系的学习扩展到one-shot分割框架中,其中前向和反向构成的循环一致性 (forward-backward cycle-consistency)在图像、转换和标签空间中起到了额外监督的重要作用 (注:这里的前向和反向不同于深度学习的前向传...
forms state-of-the art one-shot biomedical segmentation ap- proaches, including single-atlas segmentation and super- vised segmentation with hand-tuned data augmentation. 2. Related work 2.1. Medical image segmentation We focus on the segmentation of brain MR images, which is challenging for severa...
Data augmentation using learned transformations for one-shot medical image segmentationAdrian V. DalcaAmy ZhaoFrédo DurandGuha BalakrishnanJohn V. Guttag
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...
@article{ayzenberg2024protosam,title={ProtoSAM-One Shot Medical Image Segmentation With Foundational Models},author={Ayzenberg, Lev and Giryes, Raja and Greenspan, Hayit},journal={arXiv preprint arXiv:2407.07042},year={2024}}@misc{ayzenberg2024dinov2,title={DINOv2 based Self Supervised Learning Fo...
Zhao, A., Balakrishnan, G., Durand, F., Guttag, J.V., Dalca, A.V.: Data augmentation using learned transformations for one-shot medical image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8543–8553 (2019) Google Scholar Sun, ...
Data augmentation using learned transforms for one-shot medical image segmentation Amy Zhao, Guha Balakrishnan, Fredo Durand, John Guttag, Adrian V. Dalca CVPR 2019. eprint arXiv:1902.09383 Getting started Prerequisites To run this code, you will need: Python 3.6+ (Python 2.7 may work but has...
论文阅读笔记《Siamese Neural Networks for One-shot Image Recognition》,程序员大本营,技术文章内容聚合第一站。