题目:Pick the Best Pre-trained Model: Towards Transferability Estimation for Medical Image Segmentation作者单位:上海交通大学、上海 AI Lab论文网址:arxiv.org/abs/2307.1195论文代码:github.com/EndoluminalS首次发布时间:2023 年 7 月 22 日1. 主要内容 【问题】预训练模型并不总能使下游任务受益。当知识从...
Specifically, we show that it can improve the segmentation of coronavirus lesions in chest CT scans.Sagie, NimrodTel-Aviv UniversityGreenspan, HayitTel-Aviv UniversityGoldberger, JacobBar-Ilan UniversitySpringer, ChamInternational Workshop on Machine Learning in Medical Imaging...
Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and...
Pre-training a CNN on ImageNet resulted in a comparable, but not consistently higher lesion detection rate, despite the amount of training data used being 10 times larger. Conclusions: This study suggests that optimal transfer learning for medical segmentation is achieved with a similar task and ...
Full size image Deep learning prediction of 4D MRI (All source code will be publicly available upon publication). Deep learning formulation A deep network with three 2D input channels is trained using training sequences together with slices of the static volume. Each training input corresponds to ...
今天分享一篇发表在CVPR 2020上的论文:LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation (原文链接:[1])。 1 研究背景 近年来随着深度学习的快速发展,深度卷积神经网络 (DCNNs)在许多分割任务上取得很好的性能。但是对于3D医学图像分割任务,获得3D空...
Transfer learning attempts to use the knowledge learned from one task and apply it to improve the learning of a separate but similar task. This article proposes to evaluate this technique’s effectiveness in classifying images from the medical domain. The article presents a model TrFEMNet (Transfer...
Unsupervised deep consistency learning adaptation network for cardiac cross-modality structural segmentation Deep neural networks have recently been succeessful in the field of medical image segmentation; however, they are typically subject to performance degradat... L Dapeng,P Yanjun,SYG Jindong - 《...
Transfer learning (TL) plays a key role in enhancing the performance of medical image analysis by leveraging knowledge from related tasks or domains. However, existing TL-based cardiac image segmentation methods frequently focus on transferring knowledge from segmentation tasks to address the challenge ...
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learning in 3D medical images. However, it is extremely challenging to ...