標(biāo)題: Titlebook: Head and Neck Tumor Segmentation and Outcome Prediction; Third Challenge, HEC Vincent Andrearczyk,Valentin Oreiller,Adrien Depeu Conference [打印本頁] 作者: 不讓做的事 時(shí)間: 2025-3-21 16:04
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作者: 我就不公正 時(shí)間: 2025-3-21 21:39 作者: 宣傳 時(shí)間: 2025-3-22 01:53 作者: LEERY 時(shí)間: 2025-3-22 05:47
Hung Chu,Luis Ricardo De la O Arévalo,Wei Tang,Baoqiang Ma,Yan Li,Alessia De Biase,Stefan Both,JohanRe- sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index i978-1-4757-0410-5978-1-4757-0408-2作者: Liability 時(shí)間: 2025-3-22 12:30
Louis Rebaud,Thibault Escobar,Fahad Khalid,Kibrom Girum,Irène Buvat- sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is 978-1-4757-0422-8978-1-4757-0420-4作者: Breach 時(shí)間: 2025-3-22 15:46
Mingyuan Meng,Lei Bi,Dagan Feng,Jinman KimResearch (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is 作者: 仲裁者 時(shí)間: 2025-3-22 21:05
Kai Wang,Yunxiang Li,Michael Dohopolski,Tao Peng,Weiguo Lu,You Zhang,Jing Wang- sources Research (OWRR) of the Department of the Interior. Most of the references in this bibliography are the work of the center of competence on eutrophication at the University of Wisconsin. The indexes refer to the WRSIC accession number, which follows each abstract. The Significant Descriptor Index is 978-1-4757-0422-8978-1-4757-0420-4作者: 打火石 時(shí)間: 2025-3-22 21:48 作者: Alveoli 時(shí)間: 2025-3-23 02:41
,Octree Boundary Transfiner: Efficient Transformers for?Tumor Segmentation Refinement,network feature maps in addition to the raw modalities as input and selects regions of interest from these. These are then processed with a transformer network and decoded with a CNN. We evaluated our framework with Dice Similarity Coefficient (DSC) 0.76426 for the first task of the Head and Neck Tu作者: Guileless 時(shí)間: 2025-3-23 07:09
,Head and?Neck Primary Tumor and?Lymph Node Auto-segmentation for?PET/CT Scans,l deep learning frameworks, including 3D U-Net, MNet, Swin Transformer, and nnU-Net (both 2D and 3D), to segment CT and PET images of primary tumors (GTVp) and cancerous lymph nodes (GTVn) automatically. Our investigations led us to three promising models for submission. Via 5-fold cross validation 作者: capsule 時(shí)間: 2025-3-23 21:30 作者: Incompetent 時(shí)間: 2025-3-24 02:02 作者: conjunctivitis 時(shí)間: 2025-3-24 03:18
Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer,ion challenge (HECKTOR 2022). In our framework, our novelty is to incorporate radiomics as an enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS). The DeepMTS jointly learns to predict the survival risk scores of patients and the segmentation masks of tumor regions. Radiomi作者: 幼稚 時(shí)間: 2025-3-24 07:19
,Recurrence-Free Survival Prediction Under the?Guidance of?Automatic Gross Tumor Volume SegmentationHEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR) dataset. The ensemble prediction results on the testing cohort achieved aggregated Dice scores of 0.77 and 0.73 for GTVp and GTVn segmentation, respectively, and a C-index value of 0.67 for RFS prediction. The code is public作者: 信條 時(shí)間: 2025-3-24 13:09 作者: 捐助 時(shí)間: 2025-3-24 15:32 作者: 現(xiàn)任者 時(shí)間: 2025-3-24 22:55 作者: 熄滅 時(shí)間: 2025-3-25 01:06
Arnav Jain,Julia Huang,Yashwanth Ravipati,Gregory Cain,Aidan Boyd,Zezhong Ye,Benjamin H. Kann?Immer war bei Wegener zuerst die Idee, die neue Auffassung, die neue Hypothese da. Erst wenn sich in seinem Gehirn die neue Auffassung geformt und ihn in ihren Bann gezwungen hatte, warf er sich mit Macht auf die Beobachtungstatsachen, um an ihnen die Richtigkeit der neuen Erkl?rung zu erweisen, dann freilich jede Einzelheit meisterhaft nutzend.?.作者: 無可非議 時(shí)間: 2025-3-25 03:40
Seyed Masoud Rezaeijo,Ali Harimi,Mohammad R. Salmanpour?Immer war bei Wegener zuerst die Idee, die neue Auffassung, die neue Hypothese da. Erst wenn sich in seinem Gehirn die neue Auffassung geformt und ihn in ihren Bann gezwungen hatte, warf er sich mit Macht auf die Beobachtungstatsachen, um an ihnen die Richtigkeit der neuen Erkl?rung zu erweisen, dann freilich jede Einzelheit meisterhaft nutzend.?.作者: Abbreviate 時(shí)間: 2025-3-25 10:51
Head and Neck Tumor Segmentation and Outcome PredictionThird Challenge, HEC作者: 同音 時(shí)間: 2025-3-25 13:45 作者: Morsel 時(shí)間: 2025-3-25 19:31 作者: 一加就噴出 時(shí)間: 2025-3-25 21:25
,Automated Head and?Neck Tumor Segmentation from?3D PET/CT HECKTOR 2022 Challenge Report,ymph nodes from 3D CT and PET images. In this work, we describe our solution to HECKTOR 2022 segmentation task. We re-sample all images to a common resolution, crop around head and neck region, and train SegResNet semantic segmentation network from MONAI. We use 5-fold cross validation to select bes作者: alleviate 時(shí)間: 2025-3-26 03:55
,A Coarse-to-Fine Ensembling Framework for?Head and?Neck Tumor and?Lymph Segmentation in?CT and?PET lay an important role but their manual segmentations are time-consuming and laborious. In this paper, we propose a coarse-to-fine ensembling framework to segment the H &N tumor and metastatic lymph nodes automatically from Positron Emission Tomography (PET) and Computed Tomography (CT) images. The f作者: 壯觀的游行 時(shí)間: 2025-3-26 04:18
A General Web-Based Platform for Automatic Delineation of Head and Neck Gross Tumor Volumes in PET/ntation method for head and neck primary and nodal gross tumor volumes (GTVp and GTVn) segmentation in positron emission tomography/computed tomography (PET/CT) provided by the MICCAI 2022 Head and Neck Tumor Segmentation Challenge (HECKTOR 2022). Our segmentation algorithm takes nnU-Net as the back作者: excrete 時(shí)間: 2025-3-26 11:11 作者: FEMUR 時(shí)間: 2025-3-26 13:16 作者: Occupation 時(shí)間: 2025-3-26 17:29
Fusion-Based Automated Segmentation in Head and Neck Cancer via Advance Deep Learning Techniques,when designing therapeutic strategies. We set to automatically segment HNSCC using advanced deep learning techniques linked to the image fusion technique.. 883 subjects were extracted from HECKTOR-Challenge. 524 subjects were considered for the training and validation procedure, and 359 subjects as 作者: 祝賀 時(shí)間: 2025-3-26 23:17
,Stacking Feature Maps of?Multi-scaled Medical Images in?U-Net for?3D Head and?Neck Tumor Segmentatihe medical domain, it remains as challenging tasks since medical data is heterogeneous, multi-level, and multi-scale. Head and Neck Tumor Segmentation Challenge (HECKTOR) provides a platform to apply machine learning techniques to the medical image domain. HECKTOR 2022 provides positron emission tom作者: 打包 時(shí)間: 2025-3-27 05:12 作者: 聽覺 時(shí)間: 2025-3-27 06:58
,A U-Net Convolutional Neural Network with?Multiclass Dice Loss for?Automated Segmentation of?Tumors nodes (GTVn) from PET/CT images provided by the HEad and neCK TumOR segmentation challenge (HECKTOR) 2022. We utilized a multiclass Dice Loss for model training which was minimized using the AMSGrad variant of the Adam algorithm optimizer. We trained our 2D models on the axial slices of the images 作者: chlorosis 時(shí)間: 2025-3-27 13:09 作者: EXTOL 時(shí)間: 2025-3-27 15:10
,Swin UNETR for?Tumor and?Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approual task performed by radiation oncologists. Deep Learning (DL) algorithms have shown potential in creating automatic segmentations, reducing delineation time and inter-observer variation. The aim of this work was to create automatic segmentations of primary tumors (GTVp) and pathological lymph node作者: 表示向下 時(shí)間: 2025-3-27 19:38
,Simplicity Is All You Need: Out-of-the-Box nnUNet Followed by?Binary-Weighted Radiomic Model for?Semarkers towards personalized medicine. In this paper, we propose a pipeline to segment the primary and metastatic lymph nodes from fluorodeoxyglucose (FDG) positron emission tomography and computed tomography (PET/CT) head and neck (H &N) images and then predict recurrence free survival (RFS) based 作者: Pageant 時(shí)間: 2025-3-27 23:19
Radiomics-Enhanced Deep Multi-task Learning for Outcome Prediction in Head and Neck Cancer,ds have been widely used for outcome prediction from medical images. However, these methods are limited by their reliance on intractable manual segmentation of tumor regions. Recently, deep learning methods have been proposed to perform end-to-end outcome prediction so as to remove the reliance on m作者: 問到了燒瓶 時(shí)間: 2025-3-28 02:36 作者: degradation 時(shí)間: 2025-3-28 09:41 作者: Statins 時(shí)間: 2025-3-28 14:08 作者: 悄悄移動(dòng) 時(shí)間: 2025-3-28 16:22
,Towards Tumour Graph Learning for?Survival Prediction in?Head & Neck Cancer Patients,aking and treatment of such cancer, due to lesions in multiple locations and outcome variability between patients. Therefore, automated segmentation and prognosis estimation approaches can help ensure each patient gets the most effective treatment. This paper presents a framework to perform these fu作者: malapropism 時(shí)間: 2025-3-28 21:45
,Combining nnUNet and?AutoML for?Automatic Head and?Neck Tumor Segmentation and?Recurrence-Free Survary tumor and lymph nodes is a time-consuming and labor-intensive process. Also, patients that underwent radiotherapy have a high risk of regional recurrence. In this work, we used 3D nnU-Net with DiceTopK loss function to achieve automatic segmentation for head and neck primary tumor and lymph node作者: figment 時(shí)間: 2025-3-28 23:12 作者: irradicable 時(shí)間: 2025-3-29 06:06 作者: flex336 時(shí)間: 2025-3-29 07:43 作者: helper-T-cells 時(shí)間: 2025-3-29 11:48 作者: Decibel 時(shí)間: 2025-3-29 18:30
Hao Jiang,Jason Haimerl,Xuejun Gu,Weiguo Lurh?rte Schlemmerei ist das Ganze. Es kommt mir noch immer ganz unwirklich vor. Wir haben nur selten und auf kurze Augenblicke Vollgas gegeben und haben alle den Eindruck, da? die Schlitten ungef?hr das leisten, was wir von ihnen erwarten.?. Das schrieb Alfred Wegener am 30. August 1930 begeistert in作者: Indecisive 時(shí)間: 2025-3-29 21:19
Anthony Wang,Ti Bai,Dan Nguyen,Steve Jiangen Gassen zeigen manch sch?ne Palaisfront des Adels der Landeshauptstadt von Steiermark. Weit schweift der Blick vom Uhrturm, dem alten Wahrzeichen von Graz, über die langgestreckten Hügel mit ihren hellen Landh?usern in den üppigen G?rten. Breite Kastanienalleen führen hinunter zum Stadtpark auf de作者: Immortal 時(shí)間: 2025-3-30 02:18 作者: 世俗 時(shí)間: 2025-3-30 06:22
Shadab Ahamed,Luke Polson,Arman Rahmimd subsequent destruction of freshwater resources and aquatic habitats. Exploration of energy competent, cost-effective systems, which are able to sequester nutrients in wastewaters, has become a requisite. Recently, microalgae–bacterial consortia are gaining attention as an environmentally friendly 作者: LUCY 時(shí)間: 2025-3-30 08:15
Abhishek Srivastava,Debesh Jha,Bulent Aydogan,Mohamed E. Abazeed,Ulas Bagci with the Water Resources Scientific Information Center (WRSIC). It is produced wholly from the information base compris- ing material abstracted and indexed for Selected Water Resources Abstracts. The bibliography is divided into volumes according to the publication dates of the source documents. V作者: 上下倒置 時(shí)間: 2025-3-30 14:07
Hung Chu,Luis Ricardo De la O Arévalo,Wei Tang,Baoqiang Ma,Yan Li,Alessia De Biase,Stefan Both,Johanooperation with the Water Resources Scientific Information Center (WRSIC). It is produced wholly from the information base compris- ing material abstracted and indexed for Selected Water Resources Abstracts. The bibliography is divided into volumes according to the publication dates of the source do作者: Coordinate 時(shí)間: 2025-3-30 19:13
Louis Rebaud,Thibault Escobar,Fahad Khalid,Kibrom Girum,Irène Buvatperation with the Water Resources Scientific Information Center (WRSIC). It is produced wholly from the information base compris- ing material abstracted and indexed for Selected Water Resources Abstracts. The bibliography is divided into volumes according to the publication dates of the source docu作者: Prostaglandins 時(shí)間: 2025-3-30 21:53
Mingyuan Meng,Lei Bi,Dagan Feng,Jinman Kimith the Water Resources Scientific Information Center (WRSIC). It is produced wholly from the information base compris- ing material abstracted and indexed for Selected Water Resources Abstracts. The bibliography is divided into volumes according to the publication dates of the source documents. Vol作者: 商談 時(shí)間: 2025-3-31 02:42 作者: 摸索 時(shí)間: 2025-3-31 07:11 作者: aspect 時(shí)間: 2025-3-31 10:33
Vajira Thambawita,Andrea M. Stor?s,Steven A. Hicks,P?l Halvorsen,Michael A. Rieglere commercialization of low-value algal products such as fuel oils. The extremely dilute nature of microalgae cultures coupled with the small size of the algal cells makes dewatering energy intensive. This chapter provides an overview of some of the key dewatering methods applicable to microalgal bro作者: BLUSH 時(shí)間: 2025-3-31 14:30 作者: ARK 時(shí)間: 2025-3-31 18:17
Qing Lyunt problems. The larger part of petroleum and natural gas reserves is located within a small group of countries. Today’s energy system is unsustainable because of equity issues as well as environmental, economic, and geopolitical concerns that have implications far into the future. Bioenergy is one 作者: 泄露 時(shí)間: 2025-3-31 23:42
Conference proceedings 2023ernational Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, on September 22, 2022..The 22 contributions presented, as well as an overview paper, were carefully reviewed and selected from 24 submissions. This challenge aims to evaluate and compare the current sta作者: grudging 時(shí)間: 2025-4-1 05:35 作者: Finasteride 時(shí)間: 2025-4-1 09:28 作者: Canopy 時(shí)間: 2025-4-1 10:55
,Automated Head and?Neck Tumor Segmentation from?3D PET/CT HECKTOR 2022 Challenge Report,t model checkpoint. The final submission is an ensemble of 15 models from 3 runs. Our solution (team name NVAUTO) achieves the 1st place on the HECKTOR22 challenge leaderboard with an aggregated dice score of 0.78802 (..). It is implemented with Auto3DSeg (..).作者: 多嘴多舌 時(shí)間: 2025-4-1 18:13 作者: Melanoma 時(shí)間: 2025-4-1 20:10
0302-9743 urrent state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 883 delineated? PET/CT images was made available for training.?.978-3-031-27419-0978-3-031-27420-6Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 減至最低 時(shí)間: 2025-4-1 23:22
,Overview of?the?HECKTOR Challenge at?MICCAI 2022: Automatic Head and?Neck Tumor Segmentation and?Ou a total of 883 cases consisting of FDG-PET/CT images and clinical information, split into 524 training and 359 test cases. The best methods obtained an aggregated Dice Similarity Coefficient (.) of 0.788 in Task 1, and a Concordance index (C-index) of 0.682 in Task 2.作者: ENACT 時(shí)間: 2025-4-2 05:56
,A Coarse-to-Fine Ensembling Framework for?Head and?Neck Tumor and?Lymph Segmentation in?CT and?PET metastatic lymph nodes, where we proposed a ensembling refinement model. This framework is evaluated quantitatively with aggregated Dice Similarity Coefficient (DSC) of 0.77782 in the task 1 of the HECKTOR 2022 challenge[., .] as team SJTU426.作者: FLOUR 時(shí)間: 2025-4-2 09:04
,A Fine-Tuned 3D U-Net for?Primary Tumor and?Affected Lymph Nodes Segmentation in?Fused Multimodal Int binary segmentation models are chosen, one for the primary tumor and one for the lymph nodes. During testing, majority voting is applied. Our results show promising performance on the training and validation cohorts, while moderate performance was observed in the test cohort.
