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: Diabetic Foot Ulcers Grand Challenge; Third Challenge, DFU Moi Hoon Yap,Connah Kendrick,Bill Cassidy Conference proceedings 2023 The Editor

[复制链接]
楼主: Impacted
发表于 2025-3-23 12:45:26 | 显示全部楼层
https://doi.org/10.1007/978-3-663-05717-8 cross validation and Test Time Augmentation. In the validation phase of DFUC2022, HarDNet-DFUS achieved 0.7063 mean Dice and was ranked third among all participants. In the final testing phase of DFUC2022, it achieved 0.7287 mean Dice and was the first place winner. The code is available on ..
发表于 2025-3-23 17:48:38 | 显示全部楼层
发表于 2025-3-23 18:34:11 | 显示全部楼层
Jürg Kuster,Christian Bachmann,Roger Wüstcessing step. The obtained results on the DFUC2022 challenge dataset show that our improvements can boost overall performance for ulcer segmentation tasks, even in scenarios where targeted structures are heterogeneous and under high imbalance conditions in the evaluated dataset. With our approach we achieved 9th place with a Dice score of 0.6975.
发表于 2025-3-24 02:16:54 | 显示全部楼层
HarDNet-DFUS: Enhancing Backbone and Decoder of HarDNet-MSEG for Diabetic Foot Ulcer Image Segmentat cross validation and Test Time Augmentation. In the validation phase of DFUC2022, HarDNet-DFUS achieved 0.7063 mean Dice and was ranked third among all participants. In the final testing phase of DFUC2022, it achieved 0.7287 mean Dice and was the first place winner. The code is available on ..
发表于 2025-3-24 04:59:23 | 显示全部楼层
发表于 2025-3-24 09:50:02 | 显示全部楼层
Refined Mixup Augmentation for Diabetic Foot Ulcer Segmentationcessing step. The obtained results on the DFUC2022 challenge dataset show that our improvements can boost overall performance for ulcer segmentation tasks, even in scenarios where targeted structures are heterogeneous and under high imbalance conditions in the evaluated dataset. With our approach we achieved 9th place with a Dice score of 0.6975.
发表于 2025-3-24 13:57:12 | 显示全部楼层
https://doi.org/10.1007/978-3-663-05717-8 deep learning classification networks. The presence of binary-identical duplicate images in datasets used to train deep learning algorithms is a well known issue that can introduce unwanted bias which can degrade network performance. However, the effect of visually similar non-identical images is a
发表于 2025-3-24 17:39:42 | 显示全部楼层
发表于 2025-3-24 21:28:48 | 显示全部楼层
发表于 2025-3-25 01:54:28 | 显示全部楼层
 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, 2025-4-30 07:11
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