找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Medical Image Understanding and Analysis; 27th Annual Conferen Gordon Waiter,Tryphon Lambrou,Sharon Gordon Conference proceedings 2024 The

[复制链接]
楼主: 螺丝刀
发表于 2025-3-23 11:12:48 | 显示全部楼层
发表于 2025-3-23 14:41:55 | 显示全部楼层
A Deep Learning Based Approach to Semantic Segmentation of Lung Tumour Areas in Gross Pathology Imags which produced a tumour pixel-wise accuracy of 69.7% (96.8% global accuracy) and tumour area IoU score of 0.616. This work on this novel application highlights the challenges with implementing a semantic segmentation model in this domain that have not been previously documented.
发表于 2025-3-23 18:33:03 | 显示全部楼层
发表于 2025-3-23 22:23:18 | 显示全部楼层
发表于 2025-3-24 05:26:16 | 显示全部楼层
Efficient Semantic Segmentation of Nuclei in Histopathology Images Using Segformeris used as it combines the advantages of transformers and convolutional neural networks. To evaluate the performance of the models, dice evaluation metric is used. The proposed method achieved state-of-the-art results on the PanNuke dataset, with Segformer-b4 achieving a mean dice score of 0.845, an
发表于 2025-3-24 08:27:50 | 显示全部楼层
Cross-Modality Deep Transfer Learning: Application to Liver Segmentation in CT and MRIunt of training data, which is not available for MR. There are many CT datasets available compared to few MR datasets. The use of transfer learning can help to mitigate the problem of having a small amount of training data. We suggest training a U-Net deep learning model on the large publicly availa
发表于 2025-3-24 12:37:08 | 显示全部楼层
发表于 2025-3-24 17:25:00 | 显示全部楼层
发表于 2025-3-24 19:02:25 | 显示全部楼层
: Cross-Domain Cell Detection in Histopathology Images via Contextual Regularizations a reconstruction task that involves masking the high-level semantic features either stochastically or adaptively. Then, a transformer-based reconstruction head is designed to recover the original features based on partial observations. Additionally, CR can be seamlessly integrated with various dee
发表于 2025-3-25 00:26:50 | 显示全部楼层
A New Similarity Metric for Deformable Registration of MALDI–MS and MRI Imageslarity metric for deformable registration, based on the update of distance transformation values. We show that our method limits the intensity distortions while providing precisely registered images, on both synthetic and mouse brain images.
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-13 23:56
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表