標(biāo)題: Titlebook: Artificial Intelligence in Radiation Therapy; First International Dan Nguyen,Lei Xing,Steve Jiang Conference proceedings 2019 Springer Nat [打印本頁(yè)] 作者: 空隙 時(shí)間: 2025-3-21 18:04
書(shū)目名稱(chēng)Artificial Intelligence in Radiation Therapy影響因子(影響力)
書(shū)目名稱(chēng)Artificial Intelligence in Radiation Therapy影響因子(影響力)學(xué)科排名
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書(shū)目名稱(chēng)Artificial Intelligence in Radiation Therapy網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱(chēng)Artificial Intelligence in Radiation Therapy被引頻次
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書(shū)目名稱(chēng)Artificial Intelligence in Radiation Therapy年度引用學(xué)科排名
書(shū)目名稱(chēng)Artificial Intelligence in Radiation Therapy讀者反饋
書(shū)目名稱(chēng)Artificial Intelligence in Radiation Therapy讀者反饋學(xué)科排名
作者: 蝕刻 時(shí)間: 2025-3-21 23:40
Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiatio symmetry in calculating image saliency of MRI images. The ratio of mean saliency value (RSal) from the propagated nodal volume on a weekly image to the mean saliency value of the pre-treatment nodal volume was calculated to assess whether the nodal volume shrank significantly. We evaluated our meth作者: hemophilia 時(shí)間: 2025-3-22 03:45 作者: 恫嚇 時(shí)間: 2025-3-22 08:19 作者: 大暴雨 時(shí)間: 2025-3-22 09:28
A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN andod for . prostate lesion detection and classification, using input sequences of T2-weighted images, apparent diffusion coefficient (ADC) maps and high b-value diffusion-weighted images. In the first stage, a Mask R-CNN model is trained to automatically segment prostate structures. In the second stag作者: output 時(shí)間: 2025-3-22 16:41 作者: 慟哭 時(shí)間: 2025-3-22 17:57
Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions,absolute error of dose volume histogram (DVH) and voxel-based mean absolute error were used to evaluate the prediction accuracy, with [0.9%, 1.9%] at PGTV, [1.1%, 2.8%] at PTV, [2.8%, 4.4%] at Lung, [3.5%, 6.9%] at Heart, [4.2%, 5.6%] at Spinal Cord, and [1.7%, 4.8%] at Body. These encouraging resul作者: 有限 時(shí)間: 2025-3-23 01:05
Online Target Volume Estimation and Prediction from an Interlaced Slice Acquisition - A Manifold Emures as targets. Locally linear embedding (LLE) was combined with manifold alignment to establish correspondence across slice positions. Multislice target contours were generated using a LLE-based motion model for each real-time image. Motion predictions were performed using a weighted k-nearest nei作者: Urologist 時(shí)間: 2025-3-23 02:47
One-Dimensional Convolutional Network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Raditwork (one-DCN) is used for the correlation modeling. This model can predict the DVH of multiple OARs based on the individual patient’s geometry without manual removal of radiation plans with outliers. The average prediction error of the measurement focusing on the left lung, right lung, heart, spin作者: arousal 時(shí)間: 2025-3-23 08:23 作者: originality 時(shí)間: 2025-3-23 13:11
Individualized 3D Dose Distribution Prediction Using Deep Learning,tion. Qualitative measurements have showed analogous dose distributions and DVH curves compared to the true dose distribution. Quantitative measurements have demonstrated that our model can precisely predict the dose distribution with various trade-offs for different patients, with the largest mean 作者: 冒煙 時(shí)間: 2025-3-23 17:32
Deep Generative Model-Driven Multimodal Prostate Segmentation in Radiotherapy,thod includes a multi-task learning framework that combines a convolutional feature extraction and an embedded regression and classification based shape modeling. This enables the network to predict the deformable shape of an organ. We show that generative neural network-based shape modeling trained作者: LUCY 時(shí)間: 2025-3-23 18:13 作者: obnoxious 時(shí)間: 2025-3-24 02:01
CBCT-Based Synthetic MRI Generation for CBCT-Guided Adaptive Radiotherapy, CBCT to MRI, which constrains the model by forcing a one-to-one mapping. A fully convolution neural network (FCN) with U-Net architecture is used in the generator to enable end-to-end CBCT-to-MRI transformations. Dense blocks and self-attention strategy are used to learn the information to well rep作者: Suppository 時(shí)間: 2025-3-24 05:42
https://doi.org/10.1057/978-1-137-46178-0ss this, a reinforcement learning application of guided Monte Carlo tree search (GTS) was implemented, coupled with SL to guide the traversal through the tree, and update the fitness values of its nodes. To test the feasibility of GTS, 13 test prostate cancer patients were evaluated. Our results sho作者: synovial-joint 時(shí)間: 2025-3-24 09:32
Orienting Frameworks and Concepts, symmetry in calculating image saliency of MRI images. The ratio of mean saliency value (RSal) from the propagated nodal volume on a weekly image to the mean saliency value of the pre-treatment nodal volume was calculated to assess whether the nodal volume shrank significantly. We evaluated our meth作者: 內(nèi)向者 時(shí)間: 2025-3-24 13:18 作者: HEDGE 時(shí)間: 2025-3-24 17:31
Orienting Frameworks and Concepts,ed by using the deep learning model and the actual position of the prostate were compared quantitatively. Differences between the predicted target positions using DNN and their actual positions are (mean ± standard deviation) . mm, . mm, and 1.64 ± 0.28 mm in anterior-posterior, lateral, and oblique作者: 縮短 時(shí)間: 2025-3-24 19:00 作者: 畢業(yè)典禮 時(shí)間: 2025-3-24 23:15 作者: Chandelier 時(shí)間: 2025-3-25 06:26
https://doi.org/10.1007/978-981-13-1715-6absolute error of dose volume histogram (DVH) and voxel-based mean absolute error were used to evaluate the prediction accuracy, with [0.9%, 1.9%] at PGTV, [1.1%, 2.8%] at PTV, [2.8%, 4.4%] at Lung, [3.5%, 6.9%] at Heart, [4.2%, 5.6%] at Spinal Cord, and [1.7%, 4.8%] at Body. These encouraging resul作者: 圓錐體 時(shí)間: 2025-3-25 08:47 作者: 無(wú)能力 時(shí)間: 2025-3-25 14:39
Rajalakshmi Sriram,Rituparna Sarkartwork (one-DCN) is used for the correlation modeling. This model can predict the DVH of multiple OARs based on the individual patient’s geometry without manual removal of radiation plans with outliers. The average prediction error of the measurement focusing on the left lung, right lung, heart, spin作者: 糾纏,纏繞 時(shí)間: 2025-3-25 18:40
Prachee Joeg,Sneha Joshi,Rajalakshmi Sriramired brain dataset. The resulting CT scans were generated with the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) scores of 60.83?HU, 17.21?dB, and 0.8, respectively. DualGAN with perceptual loss function term and coordinate convolutional layer 作者: oxidize 時(shí)間: 2025-3-25 23:40
Men as Fathers: An Indian Perspectivetion. Qualitative measurements have showed analogous dose distributions and DVH curves compared to the true dose distribution. Quantitative measurements have demonstrated that our model can precisely predict the dose distribution with various trade-offs for different patients, with the largest mean 作者: 難取悅 時(shí)間: 2025-3-26 02:22 作者: 淡紫色花 時(shí)間: 2025-3-26 06:16 作者: Small-Intestine 時(shí)間: 2025-3-26 09:02
Fathers, Caregiving and Social Change CBCT to MRI, which constrains the model by forcing a one-to-one mapping. A fully convolution neural network (FCN) with U-Net architecture is used in the generator to enable end-to-end CBCT-to-MRI transformations. Dense blocks and self-attention strategy are used to learn the information to well rep作者: 挫敗 時(shí)間: 2025-3-26 13:37
Using Supervised Learning and Guided Monte Carlo Tree Search for Beam Orientation Optimization in Rg suboptimal and inefficient solutions. Column generation (CG) has been shown to produce superior plans compared to those of human selected beams, especially in highly non-coplanar plans such as 4π Radiotherapy. In this work, we applied AI to explore the decision space of beam orientation selection.作者: 烤架 時(shí)間: 2025-3-26 17:26 作者: 用不完 時(shí)間: 2025-3-26 22:51
Automatically Tracking and Detecting Significant Nodal Mass Shrinkage During Head-and-Neck Radiatio harmful radiation dose. In an institutional IRB-approved protocol, patients were monitored with weekly T2-weighted MRIs. Gross tumor volumes (GTV) from pre-treatment MRI were propagated to weekly MRIs via deformable image registrations (DIR) for tracking the change of GTV nodal volume and detection作者: 知識(shí)分子 時(shí)間: 2025-3-27 04:29
4D-CT Deformable Image Registration Using an Unsupervised Deep Convolutional Neural Network,cycle. It can provide valuable information on the shapes and trajectories of tumor and normal structures to guide treatment planning and improve the accuracy of tumor delineation. Respiration-induced abdominal tissue motion causes significant problems in effective irradiation of abdominal cancer pat作者: STING 時(shí)間: 2025-3-27 07:45 作者: Morose 時(shí)間: 2025-3-27 11:46
A Two-Stage Approach for Automated Prostate Lesion Detection and Classification with Mask R-CNN andd visualization of prostate tissues and lesions. Their malignancy can be diagnosed before any necessary invasive approaches, such as needle biopsy, at the risk of damage to or inflammation of the periprostatic nerves, prostate and bladder neck. However, the prostate tissue malignancy on magnetic res作者: HATCH 時(shí)間: 2025-3-27 17:26
A Novel Deep Learning Framework for Standardizing the Label of OARs in CT,ich severely hampers the collection and curation of clinical data for research purpose. Currently, data cleaning is mainly done manually, which is time-consuming. The existing methods for automatically relabeling OARs remain unpractical with real patient data, due to the inconsistent delineation and作者: somnambulism 時(shí)間: 2025-3-27 20:42 作者: expunge 時(shí)間: 2025-3-27 23:23
Voxel-Level Radiotherapy Dose Prediction Using Densely Connected Network with Dilated Convolutions,d protecting surrounding organs at risk (OARs). Automatic dose prediction can reduce manual adjustments by providing close to optimal radiotherapy planning parameters, which is studied in this work. We developed a voxel-level dose prediction framework based on an end-to-end trainable densely-connect作者: elucidate 時(shí)間: 2025-3-28 03:18 作者: 好開(kāi)玩笑 時(shí)間: 2025-3-28 08:01
One-Dimensional Convolutional Network for Dosimetry Evaluation at Organs-at-Risk in Esophageal Raditherapy treatment plans. Automatic DVH prediction is therefore very use-ful to achieve high-quality esophageal treatment planning. This paper studied stacked denoise auto-encoder (SDAE) to compute correlation between DVH and distance to target histogram (DTH) based on the fact that the geometric inf作者: Abominate 時(shí)間: 2025-3-28 11:31 作者: In-Situ 時(shí)間: 2025-3-28 18:30
Deriving Lung Perfusion Directly from CT Image Using Deep Convolutional Neural Network: A Preliminal imaging suffers from many shortcomings, including the need of exogenous contrasts, longer processing time, etc. In this study, we present a new approach to derive the lung functional images, using a deep convolutional neural network to learn and exploit the underlying functional information in the作者: Obstacle 時(shí)間: 2025-3-28 19:10 作者: Immunotherapy 時(shí)間: 2025-3-29 00:00 作者: 招致 時(shí)間: 2025-3-29 04:57
Dose Distribution Prediction for Optimal Treamtment of Modern External Beam Radiation Therapy for Nper proposes a new automatic method for predicting of dose distribution of Nasopharyngeal carcinoma (NPC) from contoured computer tomography (CT) images. The proposed method consists of two phases: (1) predicting the 2D optimal dose images of each beam from contoured CT images of a patient by convol作者: Hla461 時(shí)間: 2025-3-29 09:11 作者: 完全 時(shí)間: 2025-3-29 14:17
UC-GAN for MR to CT Image Synthesis,rsarial network (CycleGAN) is becoming an influential method, however, its image quality of synthesis is not optimal yet. In this study, we proposed a new learning method named U-Net-CycleGAN (UC-GAN) to generate synthetic CT (sCT) image for MRI-only radiation treatment planning, which integrated an作者: perpetual 時(shí)間: 2025-3-29 16:57 作者: 粉筆 時(shí)間: 2025-3-29 20:35 作者: abreast 時(shí)間: 2025-3-30 02:50 作者: dysphagia 時(shí)間: 2025-3-30 05:56
https://doi.org/10.1057/978-1-137-46178-0e investigate the feasibility of CT-only dose prediction and the profitability of additional isocenter and contour information. To evaluate the network’s performance, a 5-fold cross-validation is performed on 79 prostate patients, all treated with volumetric modulated arc therapy.作者: Deadpan 時(shí)間: 2025-3-30 11:18 作者: Concrete 時(shí)間: 2025-3-30 12:52 作者: 積習(xí)難改 時(shí)間: 2025-3-30 16:33 作者: BUST 時(shí)間: 2025-3-30 22:59 作者: Ancestor 時(shí)間: 2025-3-31 03:21 作者: 朦朧 時(shí)間: 2025-3-31 06:00
Conference proceedings 2019from 24 submissions. The papers discuss the state of radiation therapy, the state of AI and related technologies, and hope to find a pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life..作者: 漂亮 時(shí)間: 2025-3-31 09:34
0302-9743 selected from 24 submissions. The papers discuss the state of radiation therapy, the state of AI and related technologies, and hope to find a pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life..978-3-030-32485-8978-3-030-32486-5Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: BLANC 時(shí)間: 2025-3-31 17:07
https://doi.org/10.1007/978-1-4613-2425-6multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This work surpasses current benchmark levels and demonstrates a reliable AI-assisted system for SRS treatment planning for multiple brain metastases.作者: 我吃花盤(pán)旋 時(shí)間: 2025-3-31 19:54 作者: 怕失去錢(qián) 時(shí)間: 2025-4-1 00:56 作者: 表皮 時(shí)間: 2025-4-1 04:09 作者: synovial-joint 時(shí)間: 2025-4-1 07:59 作者: CORD 時(shí)間: 2025-4-1 13:11
