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: Marine Pollution – Monitoring, Management and Mitigation; Amanda Reichelt-Brushett Textbook‘‘‘‘‘‘‘‘ 2023 The Editor(s) (if applicable) and

[復(fù)制鏈接]
樓主: Sentry
11#
發(fā)表于 2025-3-23 13:23:24 | 只看該作者
12#
發(fā)表于 2025-3-23 16:48:56 | 只看該作者
13#
發(fā)表于 2025-3-23 20:13:54 | 只看該作者
14#
發(fā)表于 2025-3-24 00:41:47 | 只看該作者
Amanda Reichelt-Brushettre named Reconstruction Swin Transformer (RST) for 4D MRI. RST inherits the backbone design of the Video Swin Transformer with a novel reconstruction head introduced to restore pixel-wise intensity. A convolution network called SADXNet is used for rapid initialization of 2D MR frames before RST lear
15#
發(fā)表于 2025-3-24 06:18:53 | 只看該作者
16#
發(fā)表于 2025-3-24 10:22:31 | 只看該作者
Amanda Reichelt-Brushett,Pelli L. Howe,Anthony A. Chariton,Michael St. J. Warneion of interest from the magnetic resonance imaging. Both branches are based on convolutional neural networks. After passing the exams by the two embedding branches, the output feature vectors are concatenated, and a multi-layer perceptron is used to classify the glioma biomarkers as a multi-class p
17#
發(fā)表于 2025-3-24 12:33:38 | 只看該作者
Michelle Devlin,Jon Brodiereduce the total number of registrations required for a patient by an average factor of 27.5 while maintaining comparable registration quality. Additionally composing deformations further reduces the number of registrations by a factor of 1.86, resulting in an overall average reduction factor of 51.
18#
發(fā)表于 2025-3-24 17:31:25 | 只看該作者
19#
發(fā)表于 2025-3-24 22:35:41 | 只看該作者
Angela Carpenter,Amanda Reichelt-Brushettnce and explainability of CNN-based classification models. Additionally, we introduce an explainability metric to quantitatively evaluate the alignment of model attention with radiologist-specified regions of interest (ROIs). We demonstrate that combining the radiology reports with chest X-ray image
20#
發(fā)表于 2025-3-25 01:15:36 | 只看該作者
Michael St. J. Warne,Amanda Reichelt-Brushettnce and explainability of CNN-based classification models. Additionally, we introduce an explainability metric to quantitatively evaluate the alignment of model attention with radiologist-specified regions of interest (ROIs). We demonstrate that combining the radiology reports with chest X-ray image
 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(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-5 22:36
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(fù) 返回頂部 返回列表
平山县| 皮山县| 临泉县| 淳化县| 逊克县| 子洲县| 平罗县| 车致| 会泽县| 宿州市| 蒙自县| 洞口县| 精河县| 柳江县| 克什克腾旗| 栖霞市| 繁峙县| 綦江县| 台中县| 永安市| 三亚市| 大宁县| 唐河县| 兴仁县| 佛坪县| 平乡县| 通山县| 宝兴县| 瓮安县| 岳阳市| 花垣县| 壤塘县| 屏东县| 邵武市| 兴文县| 改则县| 宁南县| 隆回县| 集安市| 云霄县| 长阳|