標題: Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 6th International Wo Alessandro Crimi,Spyridon Bakas Conferen [打印本頁] 作者: Corrugate 時間: 2025-3-21 17:15
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影響因子(影響力)
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影響因子(影響力)學科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries網(wǎng)絡公開度
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries網(wǎng)絡公開度學科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次學科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用學科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries讀者反饋
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries讀者反饋學科排名
作者: cavity 時間: 2025-3-21 21:13 作者: Mediocre 時間: 2025-3-22 00:41 作者: 燕麥 時間: 2025-3-22 04:51
Monomeric and Polymeric Carboxylic Acids, changes may indicate poor prognosis despite lower grade histology, such as findings consistent with two grade IV infiltrating gliomas: molecular glioblastoma and diffuse midline gliomas. Detailed molecular characterization aids in optimization of treatment with the goal of improved patient outcomes.作者: giggle 時間: 2025-3-22 12:25
Macromolecular Protein Complexes Ved data through the regularization branch in a semi-supervised manner. We experimentally show that our proposed method outperforms other multi-task methods including the state-of-the-art fully supervised model when the amount of annotated data is limited.作者: 一起平行 時間: 2025-3-22 16:39 作者: 長矛 時間: 2025-3-22 18:54 作者: 售穴 時間: 2025-3-22 23:56 作者: HUMP 時間: 2025-3-23 03:56 作者: 打火石 時間: 2025-3-23 07:09 作者: analogous 時間: 2025-3-23 13:19
https://doi.org/10.1007/978-1-4684-2853-7then generated new feature representations by multi-level feature fusion module, and finally made predictions on those feature maps. The proposed MMSSD framework was evaluated on the clinical dataset, and the experiment results demonstrated that our method outperformed existing popular detectors for BM detection.作者: 刺耳的聲音 時間: 2025-3-23 14:15
MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detectionthen generated new feature representations by multi-level feature fusion module, and finally made predictions on those feature maps. The proposed MMSSD framework was evaluated on the clinical dataset, and the experiment results demonstrated that our method outperformed existing popular detectors for BM detection.作者: 腐蝕 時間: 2025-3-23 19:19 作者: 松軟 時間: 2025-3-23 22:37 作者: 魔鬼在游行 時間: 2025-3-24 03:51
Conference proceedings 2021es 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervent作者: CHOKE 時間: 2025-3-24 08:20
0302-9743 rom 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions)...*The workshop and challenges were held virtually..978-3-030-72083-4978-3-030-72084-1Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 全部 時間: 2025-3-24 14:44 作者: 要塞 時間: 2025-3-24 14:56 作者: 漫步 時間: 2025-3-24 20:37
Macromolecular Protein Complexes IVy to distinguish vessels from normal regions. What is more, to improve insufficient fine vessel segmentation caused by pixel-wise loss function, we develop a centerline loss to guide learning model to pay equal attention to small vessels and large vessels, so that the segmentation accuracy of small 作者: 不出名 時間: 2025-3-24 23:11
Surbhi Dhingra,Juhi Yadav,Janesh Kumarng loss so that the preservation of brain lesion volume is encouraged. For demonstration, the proposed method was applied to ischemic stroke lesion segmentation, and experimental results show that our method better preserves the volume of brain lesions and improves the segmentation accuracy.作者: Hemoptysis 時間: 2025-3-25 03:42
Surbhi Dhingra,Juhi Yadav,Janesh Kumares in hippocampus, which was also significantly correlated to physical disability. Conversely, morphometric measurements did not reach any statistical significance. Our study emphasized the potential of dMRI, and in particular the importance of advanced models such as 3D-SHORE with respect to DTI in作者: 蛙鳴聲 時間: 2025-3-25 08:02 作者: coagulate 時間: 2025-3-25 15:41
Guimei Yu,Yunpeng Bai,Zhong-Yin Zhangariate methods are sufficient to address lesion-anatomical bias. This is a commonly encountered situation when working with public datasets, which very often lack general health data. We support our claim with a set of simulated experiments using a publicly available lesion imaging dataset, on which作者: 歡呼 時間: 2025-3-25 16:37
Springer Series in Materials Sciencee-art 3D GAN with refinement training steps. In experiments using non-contrast computed tomography images from traumatic brain injury (TBI) patients, the model detects and localizes TBI abnormalities with an area under the ROC curve of .75.. Moreover, we test the potential of the method for detectin作者: 保守 時間: 2025-3-25 21:57
Macromolecular Science and Engineeringe segmentation performance. Our results suggest that a network trained using curriculum learning is effective at compensating for different levels of motion artifacts, and improved the segmentation performance by .9%–15% (.) when compared against a conventional shuffled learning strategy on the same作者: 機警 時間: 2025-3-26 02:02 作者: Ointment 時間: 2025-3-26 06:39 作者: Urgency 時間: 2025-3-26 10:18 作者: ENDOW 時間: 2025-3-26 14:32
Convolutional Neural Network with Asymmetric Encoding and Decoding Structure for Brain Vessel Segmeny to distinguish vessels from normal regions. What is more, to improve insufficient fine vessel segmentation caused by pixel-wise loss function, we develop a centerline loss to guide learning model to pay equal attention to small vessels and large vessels, so that the segmentation accuracy of small 作者: Nebulous 時間: 2025-3-26 17:47
Volume Preserving Brain Lesion Segmentationng loss so that the preservation of brain lesion volume is encouraged. For demonstration, the proposed method was applied to ischemic stroke lesion segmentation, and experimental results show that our method better preserves the volume of brain lesions and improves the segmentation accuracy.作者: figment 時間: 2025-3-27 00:51 作者: 口訣 時間: 2025-3-27 02:49 作者: 誤傳 時間: 2025-3-27 08:55
Multivariate Analysis is Sufficient for Lesion-Behaviour Mappingariate methods are sufficient to address lesion-anatomical bias. This is a commonly encountered situation when working with public datasets, which very often lack general health data. We support our claim with a set of simulated experiments using a publicly available lesion imaging dataset, on which作者: Decline 時間: 2025-3-27 11:28 作者: Tailor 時間: 2025-3-27 15:25
Assessing Lesion Segmentation Bias of Neural Networks on Motion Corrupted Brain MRIe segmentation performance. Our results suggest that a network trained using curriculum learning is effective at compensating for different levels of motion artifacts, and improved the segmentation performance by .9%–15% (.) when compared against a conventional shuffled learning strategy on the same作者: 周興旺 時間: 2025-3-27 20:32
Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regressioneen growing. Preoperative structural multi-parametric MRI (.) scans from . subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal FLAIR signal envelope for training one DL model for eac作者: hemorrhage 時間: 2025-3-28 01:12 作者: 該得 時間: 2025-3-28 04:10
Glioma Diagnosis and Classification: Illuminating the Gold Standardolecular features, in the context of imaging and demographic information. This paper will introduce classic histologic features of gliomas in contrast to nonneoplastic brain parenchyma, describe the basic clinical algorithm used to classify infiltrating gliomas, and demonstrate how the classificatio作者: 無節(jié)奏 時間: 2025-3-28 09:11
Multiple Sclerosis Lesion Segmentation - A Survey of Supervised CNN-Based Methodses in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works作者: 標準 時間: 2025-3-28 13:04
Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomicsdiogenomics. This has raised hopes for developing non-invasive and in-vivo biomarkers for prediction of patient survival, tumor recurrence, or molecular characterization, and therefore, encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-opera作者: CALL 時間: 2025-3-28 17:22
Automatic Segmentation of Non-tumor Tissues in Glioma MR Brain Images Using Deformable Registration ysicians. Pathological variability often renders difficulty to register a well-labeled normal atlas to such images and to automatic segment/label surrounding normal brain tissues. In this paper, we propose a new registration approach that first segments brain tumor using a U-Net and then simulates m作者: 偏離 時間: 2025-3-28 21:06 作者: 宣傳 時間: 2025-3-28 23:09
Volume Preserving Brain Lesion Segmentation brain lesion segmentation due to its accuracy and efficiency. CNNs are generally trained with loss functions that measure the segmentation accuracy, such as the cross entropy loss and Dice loss. However, lesion load is a crucial measurement for disease analysis, and these loss functions do not guar作者: Conducive 時間: 2025-3-29 05:46
Microstructural Modulations in the Hippocampus Allow to Characterizing Relapsing-Remitting Versus Prill unknown. The objective of this study was to evaluate morphometric and microstructural properties based on structural and diffusion magnetic resonance imaging (dMRI) data in these MS phenotypes, and verify if selective intra-pathological alterations characterise GM structures. Diffusion Tensor Im作者: Atmosphere 時間: 2025-3-29 08:13
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathologyor geometry through location alignment and facilitate pathological analysis. Since tumor region does not match with any ordinary brain tissue, it has been difficult to deformably register a patient’s brain to a normal one. Many patient images are associated with irregularly distributed lesions, resu作者: detach 時間: 2025-3-29 12:00 作者: CEDE 時間: 2025-3-29 17:38 作者: 嘲笑 時間: 2025-3-29 20:54
Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentationze that the spatio-temporal cues in longitudinal data can aid the segmentation algorithm. Therefore, we propose a multi-task learning approach by defining an auxiliary self-supervised task of deformable registration between two time-points to guide the neural network toward learning from spatio-temp作者: OVERT 時間: 2025-3-30 02:01 作者: Amplify 時間: 2025-3-30 07:43 作者: 南極 時間: 2025-3-30 12:14 作者: ULCER 時間: 2025-3-30 13:16 作者: 傲慢人 時間: 2025-3-30 18:51 作者: 無表情 時間: 2025-3-30 22:11 作者: Preserve 時間: 2025-3-31 04:28
https://doi.org/10.1007/978-3-319-46503-6es in a variety of medical image analysis applications has renewed community interest in this challenging problem and led to a burst of activity for new algorithm development. In this survey, we investigate the supervised CNN-based methods for MS lesion segmentation. We decouple these reviewed works作者: 舊病復發(fā) 時間: 2025-3-31 06:58
Samuel H. Becker,Huilin Li,K. Heran Darwindiogenomics. This has raised hopes for developing non-invasive and in-vivo biomarkers for prediction of patient survival, tumor recurrence, or molecular characterization, and therefore, encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-opera作者: Ptosis 時間: 2025-3-31 11:07 作者: largesse 時間: 2025-3-31 14:50
Macromolecular Protein Complexes IVn-based methods are ineffective in this challenging task due to imaging quality limits and structural complexity. And learning based methods are difficult to be used in this task due to extremely high time consumption in manually labeling and the lack of labelled open datasets. To address this, in t
