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: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging; 4th International Wo Carole H. Sudre,Christian F. Baumgartner,Will

[復制鏈接]
樓主: 請回避
11#
發(fā)表于 2025-3-23 11:43:41 | 只看該作者
On the?Pitfalls of?Entropy-Based Uncertainty for?Multi-class Semi-supervised Segmentationed solution on a challenging multi-class segmentation dataset and in two well-known uncertainty-based segmentation methods. The reported results demonstrate that by simply replacing the mechanism used to compute the uncertainty, our proposed solution brings consistent improvements.
12#
發(fā)表于 2025-3-23 15:37:01 | 只看該作者
What Do Untargeted Adversarial Examples Reveal in?Medical Image Segmentation?sults for uncertain region findings on medical image datasets while only requiring one extra inference from a pre-trained model and short iteration of attack. We expect our novel findings can provide insights for future medical image segmentation problems where detection of subtle variations (e.g.,
13#
發(fā)表于 2025-3-23 21:51:22 | 只看該作者
Improved Post-hoc Probability Calibration for?Out-of-Domain MRI Segmentationodel is tested on cardiac MRI segmentation datasets that contain unseen imaging artifacts and images from an unseen imaging protocol. We demonstrate reduced calibration errors compared with the state-of-the-art calibration algorithm.
14#
發(fā)表于 2025-3-24 01:30:13 | 只看該作者
15#
發(fā)表于 2025-3-24 03:51:27 | 只看該作者
16#
發(fā)表于 2025-3-24 06:49:34 | 只看該作者
Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-taskdicting inter-rater labeling variability visualized using a variance map of three raters’ annotations. Our technique is validated on MRIs of paraspinal muscles at four different disc levels from 118 LBP patients. Benefiting from the transformer mechanism and convolution neural networks, our algorith
17#
發(fā)表于 2025-3-24 11:31:59 | 只看該作者
Information Gain Sampling for?Active Learning in?Medical Image Classificationes including the diversity based CoreSet and uncertainty based maximum entropy sampling. Specifically, AEIG achieves . of overall performance with only 19% of the training data, while other active learning approaches require around 25%. We show that, by careful design choices, our model can be integ
18#
發(fā)表于 2025-3-24 15:31:14 | 只看該作者
19#
發(fā)表于 2025-3-24 21:16:57 | 只看該作者
20#
發(fā)表于 2025-3-25 00:00:40 | 只看該作者
 關于派博傳思  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 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, 2025-10-6 03:19
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
蚌埠市| 梧州市| 五原县| 嘉兴市| 广州市| 永丰县| 红河县| 九寨沟县| 福安市| 太原市| 汉川市| 卢龙县| 高安市| 开江县| 永和县| 烟台市| 宜昌市| 涟源市| 东丽区| 东至县| 平湖市| 鹿泉市| 交口县| 化州市| 乐山市| 平舆县| 武汉市| 吉林省| 枣强县| 九龙城区| 新乡县| 新野县| 高雄市| 咸阳市| 民和| 南康市| 铜陵市| 正宁县| 应用必备| 辛集市| 宁河县|