標(biāo)題: Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro; First International Hayit Greenspan, [打印本頁(yè)] 作者: 民俗學(xué) 時(shí)間: 2025-3-21 19:36
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro影響因子(影響力)
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro影響因子(影響力)學(xué)科排名
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro網(wǎng)絡(luò)公開度
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro被引頻次
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro被引頻次學(xué)科排名
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro年度引用
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro年度引用學(xué)科排名
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro讀者反饋
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Pro讀者反饋學(xué)科排名
作者: conduct 時(shí)間: 2025-3-21 20:36
Probabilistic Image Registration via Deep Multi-class Classification: Characterizing Uncertaintye use a deep multi-class classifier trained on different classes of patch pairs, including ., ., and a collection of discrete displacements between patches. The displacement classes alleviate the need for registration-time optimization by gradient descent; instead, posterior probabilities are used t作者: Medicaid 時(shí)間: 2025-3-22 04:03
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inferencehallenges to traditional networks. Given that medical image analysis typically requires a sequence of inference tasks to be performed (e.g. registration, segmentation), this results in an accumulation of errors over the sequence of deterministic outputs. In this paper, we explore the premise that, b作者: Microaneurysm 時(shí)間: 2025-3-22 07:25
Reg R-CNN: Lesion Detection and Grading Under Noisy Labelsrrent state-of-the-art object detectors are comprised of two stages: the first stage generates region proposals, the second stage subsequently categorizes them. Unlike in natural images, however, for anatomical structures of interest such as tumors, the appearance in the image (e.g., scale or intens作者: 收到 時(shí)間: 2025-3-22 10:53 作者: Modicum 時(shí)間: 2025-3-22 14:29
Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classificationn in the context of medical diagnosis. However, when using a neural network as a decision support tool, it is important to also quantify the (un)certainty regarding the outputs of the system. Current Bayesian techniques approximate the true predictive output distribution via sampling, and quantify t作者: 托人看管 時(shí)間: 2025-3-22 18:42
A Generalized Approach to Determine Confident Samples for Deep Neural Networks on Unseen Datarformance over traditional machine learning models. However, like any other data-driven models, DNN models still face generalization limitations. For example, a model trained on clinical data from one hospital may not perform as well on data from another hospital. In this work, a novel approach is p作者: inflate 時(shí)間: 2025-3-22 22:54 作者: output 時(shí)間: 2025-3-23 03:41 作者: liposuction 時(shí)間: 2025-3-23 07:56 作者: Infuriate 時(shí)間: 2025-3-23 12:42 作者: FUSE 時(shí)間: 2025-3-23 14:16
Recovering Physiological Changes in Nasal Anatomy with Confidence Estimateshanges make it challenging for non-deformable vision-based registration algorithms to find accurate alignments between CT image and intraoperative video. Large alignment errors can lead to injuries to critical structures. In this paper, we present a deformable video-CT registration that deforms the 作者: 元音 時(shí)間: 2025-3-23 19:59 作者: 仲裁者 時(shí)間: 2025-3-24 02:16 作者: 誰(shuí)在削木頭 時(shí)間: 2025-3-24 03:10
Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseasto automate the disease diagnosis based on clinical 2D US images since they provide partial anatomic information of the kidney and the 2D images of the same kidney may have heterogeneous appearance. To overcome this challenge, we develop a novel multi-instance deep learning method to build a robust 作者: BRIBE 時(shí)間: 2025-3-24 07:24
Data Augmentation from Sketch this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquis作者: 錯(cuò)誤 時(shí)間: 2025-3-24 10:41
Conference proceedings 2019 Imaging, UNSURE 2019, and the 8.th. International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. ..For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and 作者: 克制 時(shí)間: 2025-3-24 16:03 作者: SSRIS 時(shí)間: 2025-3-24 22:11
Conference proceedings 2019accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.?.作者: 古董 時(shí)間: 2025-3-25 02:44 作者: Alopecia-Areata 時(shí)間: 2025-3-25 05:48 作者: 富足女人 時(shí)間: 2025-3-25 07:57
Data Augmentation from Sketchween virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives.作者: 運(yùn)氣 時(shí)間: 2025-3-25 14:43
Quantifying Uncertainty of Deep Neural Networks in Skin Lesion Classificationhe uncertainty based on the variance of the output samples. In this paper, we highlight the limitations of a variance based metric, and propose a novel uncertainty metric based on the overlap of the output distributions. We show that this yields promising results on the HAM10000 dataset for skin lesion classification.作者: chondromalacia 時(shí)間: 2025-3-25 17:18 作者: curettage 時(shí)間: 2025-3-25 20:50
Probabilistic Surface Reconstruction with Unknown Correspondencence. To this end, we use the Metropolis-Hastings algorithm to sample reconstructions with unknown pose and correspondence from the posterior distribution. We introduce a projection-proposal to propose shape and pose updates to the Markov-Chain, which lets us explore the posterior distribution much m作者: isotope 時(shí)間: 2025-3-26 00:18 作者: HAIL 時(shí)間: 2025-3-26 05:22
Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inferencethologies (e.g. lesions, tumours) in brain MR images. We apply the framework to two different contexts. First, we demonstrate that propagating multiple sclerosis T2 lesion segmentation results along with their associated uncertainty measures improves subsequent T2 lesion detection accuracy when eval作者: 嬰兒 時(shí)間: 2025-3-26 10:43
Reg R-CNN: Lesion Detection and Grading Under Noisy Labels, we propose Reg R-CNN, which replaces the second-stage classification model of a current object detector with a regression model. We show the superiority of our approach on a public data set with 1026 patients and a series of toy experiments. Code will be available at ..作者: Introduction 時(shí)間: 2025-3-26 13:34
Fast Nonparametric Mutual-Information-based Registration and Uncertainty Estimationle additional code and no external tuning. Preliminary experiments on multi-modal brain MRI images show that the proposed optimizer can be both faster and more accurate than the free-form deformation method implemented in Elastix. We also demonstrate the sampler’s ability to produce direct uncertain作者: 記成螞蟻 時(shí)間: 2025-3-26 20:07 作者: Flounder 時(shí)間: 2025-3-27 00:06 作者: 圓錐體 時(shí)間: 2025-3-27 02:55
Spatiotemporal Statistical Model of Anatomical Landmarks on a Human Embryonic Brain. We conducted a comparative study of the spatiotemporal statistical models between four different prediction methods, and we found that deformable surface mapping was the best prediction method in terms of model generalization and specificity.作者: eustachian-tube 時(shí)間: 2025-3-27 07:41
Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Iowed that the proposed intestine segmentation method produced higher Dice score (0.68) than traditional RST analysis (0.44), even without specific refinement processes like machine-learning-based false positive reduction.作者: 頭盔 時(shí)間: 2025-3-27 11:13 作者: 修正案 時(shí)間: 2025-3-27 17:26
DPANet: A Novel Network Based on Dense Pyramid Feature Extractor and Dual Correlation Analysis Attential-channel soft attention modules before each deconvolution operation in the decoder phase, which can better reintegrate features and support clear semantic similarity for network. All these unique operations boost the performance of our method on gland segmentation task. We achieve state-of-the-a作者: 攝取 時(shí)間: 2025-3-27 19:01 作者: gene-therapy 時(shí)間: 2025-3-28 00:55
Dennis Madsen,Thomas Vetter,Marcel Lüthige (ABAQUS) and primarily serves as an ideation tool, where designers can sketch different architectures composed of FRPAMs and analyze their force-deformation characteristics. Furthermore, such a model can also be useful in integrating with topology optimization routines to synthesize optimal spatial arrangement of actuators.作者: 時(shí)代錯(cuò)誤 時(shí)間: 2025-3-28 03:37 作者: synovial-joint 時(shí)間: 2025-3-28 09:30 作者: orthopedist 時(shí)間: 2025-3-28 13:57
reseen by many organizations that the energy consumption in the future will be much higher than what is used today. For this reason, this study investigates the usage areas of the existing raw materials and the anticipated raw material requirements in the future.作者: absolve 時(shí)間: 2025-3-28 17:20 作者: Gossamer 時(shí)間: 2025-3-28 22:46
Debora Gil,Antonio Esteban-Lansaque,Sebastian Stefaniga,Mihail Gaianu,Carles Sancheze mortgage” pension model the importance of the implementation in the Chongqing region, feasibility and market acceptance and other detailed analysis of proposed measures “reverse mortgage” policy successfully implemented in Chongqing. Reference was provided for relevant government departments.作者: 楓樹 時(shí)間: 2025-3-29 02:04 作者: guzzle 時(shí)間: 2025-3-29 04:19 作者: 哄騙 時(shí)間: 2025-3-29 07:19 作者: 繁殖 時(shí)間: 2025-3-29 11:26
Pieter Van Molle,Tim Verbelen,Cedric De Boom,Bert Vankeirsbilck,Jonas De Vylder,Bart Diricx,Tom Kimpg and foundation soil. It has been observed that for specific levels of lateral loading, the lateral displacement in the building frame may even increase by 100% owing to the reduction in the soil stiffness, which calls for special attention to such problems.作者: Coronary-Spasm 時(shí)間: 2025-3-29 17:55
Min Zhang,Kevin H. Leung,Zili Ma,Jin Wen,Gopal Avinashnd utilization of the mansion sites in the Qing Dynasty in Lingnan area of China, and can also provide a reference mode and model for the study of similar mansion protection and utilization in China and abroad.作者: febrile 時(shí)間: 2025-3-29 23:15
Aoi Shinjo,Atsushi Saito,Tetsuya Takakuwa,Shigehito Yamada,Hidekata Hontani,Hiroshi Matsuzoe,Shoko Mmeasured with error free application and 88% application usability survey. This system will contribute to the work of a strategy intended to improve business and customer satisfaction when it comes to vehicle repair processes.作者: heirloom 時(shí)間: 2025-3-30 03:46
Hirohisa Oda,Kohei Nishio,Takayuki Kitasaka,Benjamin Villard,Hizuru Amano,Kosuke Chiba,Akinari Hinokign can ensure the bearing capacity of the cable when hoisting the main beam. In the discussed study case, the successful application of the cable hoisting system can prove a method and reference to similar construction.作者: 挖掘 時(shí)間: 2025-3-30 07:53
Luise Middel,Christoph Palm,Marius Erdt guide cultivation practices and marketing strategies with more comprehensive data while examining how this research contributes to the classification of agricultural practices and the application of machine learning in a broader range of industrial sectors.作者: 草率男 時(shí)間: 2025-3-30 08:18 作者: Diluge 時(shí)間: 2025-3-30 15:07
