標(biāo)題: Titlebook: Kidney and Kidney Tumor Segmentation; MICCAI 2021 Challeng Nicholas Heller,Fabian Isensee,Christopher Weight Conference proceedings 2022 Sp [打印本頁] 作者: Coenzyme 時(shí)間: 2025-3-21 20:08
書目名稱Kidney and Kidney Tumor Segmentation影響因子(影響力)
書目名稱Kidney and Kidney Tumor Segmentation影響因子(影響力)學(xué)科排名
書目名稱Kidney and Kidney Tumor Segmentation網(wǎng)絡(luò)公開度
書目名稱Kidney and Kidney Tumor Segmentation網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Kidney and Kidney Tumor Segmentation被引頻次
書目名稱Kidney and Kidney Tumor Segmentation被引頻次學(xué)科排名
書目名稱Kidney and Kidney Tumor Segmentation年度引用
書目名稱Kidney and Kidney Tumor Segmentation年度引用學(xué)科排名
書目名稱Kidney and Kidney Tumor Segmentation讀者反饋
書目名稱Kidney and Kidney Tumor Segmentation讀者反饋學(xué)科排名
作者: Exonerate 時(shí)間: 2025-3-21 22:29 作者: Allure 時(shí)間: 2025-3-22 03:43
,Extraction of?Kidney Anatomy Based on?a?3D U-ResNet with?Overlap-Tile Strategy,, while a rule-based postprocessing was applied to remove false-positive artefacts. Our model achieved 0.812 average dice, 0.694 average surface dice and 0.7 tumor dice. This led to the 12.5th position in the KiTS21 challenge.作者: 惰性女人 時(shí)間: 2025-3-22 08:23
,An Ensemble of?3D U-Net Based Models for?Segmentation of?Kidney and?Masses in?CT Scans,tions, including the use of transfer learning, an unsupervised regularized loss, custom postprocessing, and multi-annotator ground truth that mimics the evaluation protocol. Our submission has reached the 2nd place in the KiTS21 challenge.作者: fatuity 時(shí)間: 2025-3-22 09:53
,Automated Kidney Tumor Segmentation with?Convolution and?Transformer Network,r improve segmentation performance. Experimental results on the 2021 kidney and kidney tumor segmentation (kits21) challenge demonstrated that our method achieved average dice of 61.6%, surface dice of 49.1%, and tumor dice of 50.52%, respectively, which ranked the . place on the kits21 challenge.作者: conscience 時(shí)間: 2025-3-22 13:02
,A Two-Stage Cascaded Deep Neural Network with?Multi-decoding Paths for?Kidney Tumor Segmentation,. We evaluated our method on the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) dataset. The method achieved Dice score, Surface Dice and Tumor Dice of 69.4%, 56.9% and 51.9% respectively, in the test cases. The model of cascade network proposed in this paper has a promising application prospect in kidney cancer diagnosis.作者: Aqueous-Humor 時(shí)間: 2025-3-22 17:50
2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst,work ResSENormUnet; then, the kidney and the tumor and cyst are fine-segmented by the second stage network DenseTransUnet, and finally, a post-processing operation based on a 3D connected region is used for the removal of false-positive segmentation results. We evaluate this approach in the KiTS21 challenge, which shows promising performance.作者: 背帶 時(shí)間: 2025-3-22 21:56 作者: 繁榮地區(qū) 時(shí)間: 2025-3-23 03:21 作者: 離開就切除 時(shí)間: 2025-3-23 08:49
,A Coarse-to-Fine Framework for?the?2021 Kidney and?Kidney Tumor Segmentation Challenge,ion of the kidney and kidney mass. The average Dice and surface Dice of segmentation predicted by our method on the test are 0.9077 and 0.8262, respectively. Our method outperformed all other teams and achieved . in the KITS2021 challenge.作者: 吹牛者 時(shí)間: 2025-3-23 10:03
,Automatic Segmentation in?Abdominal CT Imaging for?the?KiTS21 Challenge,Net which consists of 3D Encoder-Decoder CNN architecture with additional Skip Connection is used. Lastly, there is a loss function to resolve the class imbalance problem frequently occurring in the task of medical imaging. S?rensen-Dice Score and Surface Dice Score on the test set are 80.13 and 68.61.作者: Permanent 時(shí)間: 2025-3-23 14:26 作者: 大包裹 時(shí)間: 2025-3-23 21:25
Conference proceedings 2022er 27, 2021, due to the COVID-19 pandemic...The 21 contributions presented were carefully reviewed and selected from 29 submissions. This challenge aims to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. .作者: OFF 時(shí)間: 2025-3-23 23:10 作者: Allodynia 時(shí)間: 2025-3-24 06:21
Modified nnU-Net for the MICCAI KiTS21 Challenge,e model by specific strategies. Detailed information is available in the part of Methods. The organizer uses an evaluation method called “Hierarchical Evaluation Classes” (HECs). The HEC scores of each model are showed in the following.作者: Immunization 時(shí)間: 2025-3-24 09:35 作者: 協(xié)定 時(shí)間: 2025-3-24 11:10 作者: 抵押貸款 時(shí)間: 2025-3-24 15:51
3D U-Net Based Semantic Segmentation of Kidneys and Renal Masses on Contrast-Enhanced CT,he majority-prediction segmentation masks. Our model achieved test-set performance of 97.0%, 85.1%, and 81.9% volumetric Dice score, and 93.7%, 72.0%, and 70.0% surface Dice score, on combined foreground, renal masses, and renal tumors, respectively, which tied for sixth place among challenge participants.作者: 骯臟 時(shí)間: 2025-3-24 21:52 作者: EXULT 時(shí)間: 2025-3-25 01:47 作者: ABHOR 時(shí)間: 2025-3-25 05:11
Kidney and Kidney Tumor Segmentation978-3-030-98385-7Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: VEN 時(shí)間: 2025-3-25 10:19 作者: hazard 時(shí)間: 2025-3-25 11:39 作者: glomeruli 時(shí)間: 2025-3-25 16:32 作者: insipid 時(shí)間: 2025-3-25 23:22
Zhiqiang Shen,Hua Yang,Zhen Zhang,Shaohua Zhenger is denoted by ≤. If . is a family of partial orders on . such that (i)??.,.?∈?.,.?≤?. implies ., and (ii)., then call . a poset with partial order family .(.-poset for short),denoted by .. It provides possibility to interpret or measure the complex information in stepwise computing. We will write作者: 轎車 時(shí)間: 2025-3-26 03:17
Jannes Adam,Niklas Agethen,Robert Bohnsack,René Finzel,Timo Günnemann,Lena Philipp,Marcel Plutat,Mare conf- ence proceedingsis published by Springer-Verlag(Advancesin Soft Computing,ISSN: 1615-3871). This year, we have received 155 submissions. Each paper has undergone a rigorous review process. Only high-quality papers are included. The Third Annual Conference on Fuzzy Information and Engineering作者: Misnomer 時(shí)間: 2025-3-26 06:32
Lizhan Xu,Jiacheng Shi,Zhangfu Dongm (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast en-hancement algorithms, which search optimal gray transform parameters in the whole作者: arthroscopy 時(shí)間: 2025-3-26 09:35
Zhiwei Chen,Hanqiang Lium (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast en-hancement algorithms, which search optimal gray transform parameters in the whole作者: 鑲嵌細(xì)工 時(shí)間: 2025-3-26 13:47
Vivek Pawar,Bharadwaj Kssm (IBT) is used to obtain non-linear gray transform curve so as to enhance global contrast for an image. GA determines optimal gray transform parameters. In order to avoid the expensive time for traditional contrast en-hancement algorithms, which search optimal gray transform parameters in the whole作者: propose 時(shí)間: 2025-3-26 18:22
Zhongchen Zhao,Huai Chen,Lisheng Wang have deformations, connected strokes and overlapped characters, and they often occurre with punctuations and digital numrbers. The multi-step offline handwritten Chinese characters segmentation method based on adaptive genetic algorithm was put forward in this paper to segment connected or overlaps作者: 容易懂得 時(shí)間: 2025-3-27 00:46
Chaonan Lin,Rongda Fu,Shaohua Zhengof the sky-high nonlinearity of the visual process itself, using nonlinear signal processing technology, which becomes an important trend on the field of images naturally. Mathematical morphology is a rather unique nonlinear theory, the paper study the key technical problems specifically, which solv作者: ineluctable 時(shí)間: 2025-3-27 03:46
Jianhui Wen,Zhaopei Li,Zhiqiang Shen,Yaoyong Zheng,Shaohua Zheng the 2016 International Workshop on Mathematics and Decision.This book introduces applications of mathematics and fuzzy mathematics in decision science, fuzzy geometric programming and fuzzy optimization as well as operations research and management, based on 44 research papers presented at three su作者: Cerumen 時(shí)間: 2025-3-27 05:40
Tian He,Zhen Zhang,Chenhao Pei,Liqin Huang the 2016 International Workshop on Mathematics and Decision.This book introduces applications of mathematics and fuzzy mathematics in decision science, fuzzy geometric programming and fuzzy optimization as well as operations research and management, based on 44 research papers presented at three su作者: 規(guī)范要多 時(shí)間: 2025-3-27 12:08 作者: 六個(gè)才偏離 時(shí)間: 2025-3-27 14:58 作者: groggy 時(shí)間: 2025-3-27 17:53
Alex Golts,Daniel Khapun,Daniel Shats,Yoel Shoshan,Flora Gilboa-Solomonof hotel marketing quality on the basis of fuzzy clustering. The information of different types of 1579 hotels in Guangzhou was manually collected from the eLong net, including room characteristics, customer reviews, hotel service information, and reservation platform recommendation. Based on the in作者: 紅腫 時(shí)間: 2025-3-28 00:48 作者: 他姓手中拿著 時(shí)間: 2025-3-28 04:56 作者: constitutional 時(shí)間: 2025-3-28 09:35
Christina B. Lund,Bas H. M. van der Velden attributed graph sequence contains edge-oriented structural data and vertex-oriented attribute data both of which vary with time. By reviewing the different forms of temporal association rules in the literature of related date types, we propose a definition of temporal association rule of an attrib作者: 服從 時(shí)間: 2025-3-28 12:53 作者: Expressly 時(shí)間: 2025-3-28 14:46 作者: SEED 時(shí)間: 2025-3-28 20:51 作者: Rejuvenate 時(shí)間: 2025-3-29 02:25 作者: Accessible 時(shí)間: 2025-3-29 05:27
Modified nnU-Net for the MICCAI KiTS21 Challenge,ataset of 300 cases and each case’s CT scan is segmented to three semantic classes: Kidney, Tumor and Cyst. Compared with KiTS19 Challenge, cyst is a new semantic class, but these two tasks are quite?close and that is why we choose nnUNet as our model and made some adjustments on it. Some important 作者: 富饒 時(shí)間: 2025-3-29 08:18
2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst,works to automatically segment kidney and tumor and cyst in computed tomography (CT) images. First, the kidney is pre-segmented by the first stage network ResSENormUnet; then, the kidney and the tumor and cyst are fine-segmented by the second stage network DenseTransUnet, and finally, a post-process作者: archetype 時(shí)間: 2025-3-29 11:56 作者: MAG 時(shí)間: 2025-3-29 17:04 作者: 奇思怪想 時(shí)間: 2025-3-29 23:08
Less is More: Contrast Attention Assisted U-Net for Kidney, Tumor and Cyst Segmentations,on tasks. We argue that the skip connections between the encoder and decoder layers pass too many redundant information, and filtered out the unnecessary information may be helpful in improving the segmentation accuracy. In this paper, we proposed a contrast attention mechanism at the skip connectio作者: 節(jié)省 時(shí)間: 2025-3-30 03:50
,A Coarse-to-Fine Framework for?the?2021 Kidney and?Kidney Tumor Segmentation Challenge,tool for kidney cancer surgery. In this paper, we use a coarse-to-fine framework which is based on the nnU-Net and achieve accurate and fast segmentation of the kidney and kidney mass. The average Dice and surface Dice of segmentation predicted by our method on the test are 0.9077 and 0.8262, respec作者: FRAX-tool 時(shí)間: 2025-3-30 04:56 作者: Buttress 時(shí)間: 2025-3-30 10:57
Squeeze-and-Excitation Encoder-Decoder Network for Kidney and Kidney Tumor Segmentation in CT Imageffective control methods. The precise and automatic segmentation of kidney tumors in computed tomography (CT) is an important prerequisite for medical methods such as pathological localization and radiotherapy planning, However, due to the large differences in the shape, size, and location of kidney作者: Infantry 時(shí)間: 2025-3-30 13:02
,A Two-Stage Cascaded Deep Neural Network with?Multi-decoding Paths for?Kidney Tumor Segmentation,or kidney cancer diagnosis. Automatic and accurate kidney and kidney tumor segmentation in CT scans is crucial for treatment and surgery planning. However, kidney tumors and cysts have various morphologies, with blurred edges and unpredictable positions. Therefore, precise segmentation of tumors and作者: Lipoprotein(A) 時(shí)間: 2025-3-30 20:26 作者: 允許 時(shí)間: 2025-3-31 00:32
,Automatic Segmentation in?Abdominal CT Imaging for?the?KiTS21 Challenge,t. Convolutional Neural Network is trained in patches of three-dimensional abdominal CT imaging. For the segmentation of the 3D image, a variant of U-Net which consists of 3D Encoder-Decoder CNN architecture with additional Skip Connection is used. Lastly, there is a loss function to resolve the cla作者: 不自然 時(shí)間: 2025-3-31 04:01 作者: 令人苦惱 時(shí)間: 2025-3-31 08:34 作者: headlong 時(shí)間: 2025-3-31 11:21 作者: Maximize 時(shí)間: 2025-3-31 15:10
Leveraging Clinical Characteristics for Improved Deep Learning-Based Kidney Tumor Segmentation on Cnced computed tomography (CT). A total of 300 kidney cancer patients with contrast-enhanced CT scans and clinical characteristics were included. A baseline segmentation of the kidney cancer was performed using a 3D U-Net. Input to the U-Net were the contrast-enhanced CT images, output were segmentat作者: Pelvic-Floor 時(shí)間: 2025-3-31 20:59 作者: 冒煙 時(shí)間: 2025-3-31 22:30
