找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support; Third International M. Jorge Cardoso,Tal Ar

[復制鏈接]
樓主: T-Lymphocyte
41#
發(fā)表于 2025-3-28 17:29:53 | 只看該作者
Context-Based Normalization of Histological Stains Using Deep Convolutional Features excellent normalization results and ensure a consistent representation of color and texture. The evaluation comprises a comparison of color histogram deviations, structural similarity and measures the color volume obtained by the different methods.
42#
發(fā)表于 2025-3-28 20:41:37 | 只看該作者
Zixuan Zhao,Yan Wang,Xiaorui Gongng, this loss function is optimised together with the conventional average per-voxel cross entropy loss..The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.
43#
發(fā)表于 2025-3-29 00:20:24 | 只看該作者
Xiao Han,Nizar Kheir,Davide Balzarotties and tested on another 124 images, all collected from real life medical emergency rooms. The results show a significant improvement in performance over the previous state-of-the-art cervical vertebrae localization framework.
44#
發(fā)表于 2025-3-29 04:26:41 | 只看該作者
45#
發(fā)表于 2025-3-29 09:56:20 | 只看該作者
Lecture Notes in Computer Scienceers of varying sizes to encourage class-specific filters at multiple spatial resolutions. We demonstrate the performance of the transition module in AlexNet and ZFNet, for classifying breast tumors in two independent datasets of scanned histology sections, of which the transition module was superior.
46#
發(fā)表于 2025-3-29 14:29:56 | 只看該作者
Adversarial Training and Dilated Convolutions for Brain MRI Segmentationng, this loss function is optimised together with the conventional average per-voxel cross entropy loss..The results show improved segmentation performance using this adversarial training procedure for segmentation of two different sets of images and using two different network architectures, both visually and in terms of Dice coefficients.
47#
發(fā)表于 2025-3-29 16:35:24 | 只看該作者
Region-Aware Deep Localization Framework for?Cervical Vertebrae in X-Ray Imageses and tested on another 124 images, all collected from real life medical emergency rooms. The results show a significant improvement in performance over the previous state-of-the-art cervical vertebrae localization framework.
48#
發(fā)表于 2025-3-29 23:01:09 | 只看該作者
49#
發(fā)表于 2025-3-30 03:52:01 | 只看該作者
50#
發(fā)表于 2025-3-30 05:14:09 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛論文網(wǎng) 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經(jīng)驗總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-13 23:14
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復 返回頂部 返回列表
图木舒克市| 平遥县| 青铜峡市| 平江县| 大兴区| 十堰市| 临城县| 涟源市| 武陟县| 河南省| 临安市| 江安县| 仁寿县| 溆浦县| 盐山县| 东兴市| 开化县| 桃源县| 平谷区| 宜黄县| 康平县| 临夏市| 安陆市| 奇台县| 广州市| 泽库县| 库伦旗| 普定县| 永嘉县| 昭觉县| 新乐市| 石门县| 镇康县| 西乌珠穆沁旗| 乌鲁木齐县| 阿巴嘎旗| 龙州县| 毕节市| 鹤山市| 阳信县| 栾川县|