標(biāo)題: Titlebook: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging; 4th International Wo Carole H. Sudre,Christian F. Baumgartner,Will [打印本頁] 作者: 請(qǐng)回避 時(shí)間: 2025-3-21 18:11
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書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
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書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging讀者反饋
書目名稱Uncertainty for Safe Utilization of Machine Learning in Medical Imaging讀者反饋學(xué)科排名
作者: babble 時(shí)間: 2025-3-21 22:58 作者: 四海為家的人 時(shí)間: 2025-3-22 02:40 作者: 瘙癢 時(shí)間: 2025-3-22 05:34
https://doi.org/10.1007/978-3-031-16749-2artificial intelligence; bayesian; bayesian networks; bioinformatics; computer vision; deep learning; imag作者: 不再流行 時(shí)間: 2025-3-22 09:56 作者: 色情 時(shí)間: 2025-3-22 13:26
Luke Whitbread,Mark JenkinsonThis book is a collection of Ed Freeman’s most influential and important works on stakeholder theory as well as business ethics, humanities, and capitalism..978-3-031-04566-0978-3-031-04564-6Series ISSN 0925-6733 Series E-ISSN 2215-1680 作者: Factual 時(shí)間: 2025-3-22 20:31
Matthew Baugh,Jeremy Tan,Athanasios Vlontzos,Johanna P. Müller,Bernhard Kainzroximate model is established as an efficient method for RCS estimation of phased arrays. This book presents a detailed formulation of approximate method to determine RCS of phased arrays, which is explained us978-981-287-753-6978-981-287-754-3Series ISSN 2191-8112 Series E-ISSN 2191-8120 作者: 大笑 時(shí)間: 2025-3-22 22:36 作者: figure 時(shí)間: 2025-3-23 04:08 作者: 身心疲憊 時(shí)間: 2025-3-23 06:11
Uncertainty Categories in?Medical Image Segmentation: A Study of?Source-Related Diversitythat should be captured whenever uncertainties are used. We take the well characterised BraTS challenge dataset to demonstrate that there are substantial differences in both magnitude and spatial pattern of uncertainties from the different categories, and discuss the implications of these in various作者: 偏狂癥 時(shí)間: 2025-3-23 11:43
On the?Pitfalls of?Entropy-Based Uncertainty for?Multi-class Semi-supervised Segmentationed solution on a challenging multi-class segmentation dataset and in two well-known uncertainty-based segmentation methods. The reported results demonstrate that by simply replacing the mechanism used to compute the uncertainty, our proposed solution brings consistent improvements.作者: Conjuction 時(shí)間: 2025-3-23 15:37
What Do Untargeted Adversarial Examples Reveal in?Medical Image Segmentation?sults for uncertain region findings on medical image datasets while only requiring one extra inference from a pre-trained model and short iteration of attack. We expect our novel findings can provide insights for future medical image segmentation problems where detection of subtle variations (e.g., 作者: Functional 時(shí)間: 2025-3-23 21:51
Improved Post-hoc Probability Calibration for?Out-of-Domain MRI Segmentationodel is tested on cardiac MRI segmentation datasets that contain unseen imaging artifacts and images from an unseen imaging protocol. We demonstrate reduced calibration errors compared with the state-of-the-art calibration algorithm.作者: 官僚統(tǒng)治 時(shí)間: 2025-3-24 01:30 作者: Trigger-Point 時(shí)間: 2025-3-24 03:51 作者: AGONY 時(shí)間: 2025-3-24 06:49
Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-taskdicting inter-rater labeling variability visualized using a variance map of three raters’ annotations. Our technique is validated on MRIs of paraspinal muscles at four different disc levels from 118 LBP patients. Benefiting from the transformer mechanism and convolution neural networks, our algorith作者: cochlea 時(shí)間: 2025-3-24 11:31
Information Gain Sampling for?Active Learning in?Medical Image Classificationes including the diversity based CoreSet and uncertainty based maximum entropy sampling. Specifically, AEIG achieves . of overall performance with only 19% of the training data, while other active learning approaches require around 25%. We show that, by careful design choices, our model can be integ作者: interpose 時(shí)間: 2025-3-24 15:31 作者: 徹底明白 時(shí)間: 2025-3-24 21:16 作者: Simulate 時(shí)間: 2025-3-25 00:00 作者: Merited 時(shí)間: 2025-3-25 05:29
Prerak Mody,Nicolas F. Chaves-de-Plaza,Klaus Hildebrandt,Marius Staringur alongside the interventions in Kosovo, Iraq and Afghanistan, the book discusses how these cases influenced current decision-making with regards to foreign interventions and offers a triangular framework through which to understand R2P: responsibility to prevent, react and rebuild.?.978-3-030-07657-3978-3-319-78831-9作者: 澄清 時(shí)間: 2025-3-25 09:33 作者: glans-penis 時(shí)間: 2025-3-25 14:51
Jacob Carse,Andres Alvarez Olmo,Stephen McKennaheir physical interaction properties in various affinity-based protocols but also as a result of genetic and computational approaches. This selection of alternative binding partners for oncogenic Ras-proteins can thus serve as a source for more in depth investigations of particular Ras-related pheno作者: inflame 時(shí)間: 2025-3-25 17:36 作者: generic 時(shí)間: 2025-3-25 23:42
Parinaz Roshanzamir,Hassan Rivaz,Joshua Ahn,Hamza Mirza,Neda Naghdi,Meagan Anstruther,Michele C. Bat directly, and through comorbidities (Greenberg, Fournier, Sisitsky, Pike, & Kessler, 2015). In this sense, in the United States, depression-related costs have increased by 21.5% between 2000 and 2015, most expenditure due to workplace costs (50%) and direct costs (45%) (Greenberg et al., 2015). The作者: 蚊帳 時(shí)間: 2025-3-26 02:15
Raghav Mehta,Changjian Shui,Brennan Nichyporuk,Tal Arbel Outline best REBT practices in assessment and treatment of the client(s)...·???????? Survey evidence-based non-REBT approaches most useful in complementing REBT...·???????? Provide a brief case example representing appropriate REBT in action...·???????? Assess their use of REBT in treating the prob作者: 停止償付 時(shí)間: 2025-3-26 08:14 作者: impaction 時(shí)間: 2025-3-26 09:10 作者: lacrimal-gland 時(shí)間: 2025-3-26 14:01
Carole H. Sudre,Christian F. Baumgartner,William M作者: floaters 時(shí)間: 2025-3-26 16:55 作者: WAIL 時(shí)間: 2025-3-26 21:35
Quantification of?Predictive Uncertainty via?Inference-Time Samplingeterministic network without changes to the architecture nor training procedure. Experiments on regression tasks on imaging and non-imaging input data show the method’s ability to generate diverse and multi-modal predictive distributions and how estimated uncertainty correlates with prediction error.作者: HAUNT 時(shí)間: 2025-3-27 03:09
nnOOD: A Framework for?Benchmarking Self-supervised Anomaly Localisation Methods. By isolating the synthetic, self-supervised task from the rest of the training process we perform a more faithful comparison of the tasks, whilst also making the workflow for evaluating over a given dataset quick and easy. Using this we have implemented the current state-of-the-art tasks and evaluated them on a challenging X-ray dataset.作者: 意外的成功 時(shí)間: 2025-3-27 07:30 作者: 別名 時(shí)間: 2025-3-27 10:39 作者: reception 時(shí)間: 2025-3-27 16:24
Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty of models by sampling along the eigen directions of the Hessian of the converged minimum. We demonstrate the utility of our methods on two challenging medical ultrasound imaging problems - cardiac view recognition and kidney segmentation.作者: 禁令 時(shí)間: 2025-3-27 17:46 作者: 暗諷 時(shí)間: 2025-3-27 22:55
Quantification of?Predictive Uncertainty via?Inference-Time Sampling that are trained to predict uncertainty estimates as variables of interest. These approaches require distinct architectural components and training mechanisms, may include restrictive assumptions and exhibit overconfidence, i.e., high confidence in imprecise predictions. In this work, we propose a 作者: Hectic 時(shí)間: 2025-3-28 05:53
Uncertainty Categories in?Medical Image Segmentation: A Study of?Source-Related Diversitylping build confidence with end users, and for improving the training and performance of the networks. Several different methods have been proposed to estimate uncertainties, including those from epistemic (relating to the model used) and aleatoric (relating to the data) sources using test-time drop作者: 痛苦一生 時(shí)間: 2025-3-28 08:12 作者: 色情 時(shí)間: 2025-3-28 13:13
What Do Untargeted Adversarial Examples Reveal in?Medical Image Segmentation?tion tasks regardless of the ground truth. To explore and identify the uncertain regions, we propose a post-training method with untargeted adversarial examples where the input image is iteratively perturbed in a direction that maximizes the loss of original and perturbed prediction. The perturbed p作者: 起波瀾 時(shí)間: 2025-3-28 17:52
Improved Post-hoc Probability Calibration for?Out-of-Domain MRI Segmentationdeep networks interpretable, by aligning prediction probability with the actual accuracy in test data. In image segmentation, well-calibrated probabilities allow radiologists to identify regions where model-predicted segmentations are unreliable. These unreliable predictions often occur to out-of-do作者: 大酒杯 時(shí)間: 2025-3-28 22:33
Improving Error Detection in Deep Learning Based Radiotherapy Autocontouring Using Bayesian Uncertaiond to inaccurate regions. To help speed up the mandatory quality assessment (QA) of contours in radiotherapy, these heatmaps could be used as stimuli to direct visual attention of clinicians to potential inaccuracies. In practice, this is non-trivial to achieve since many accurate regions also exhi作者: 走調(diào) 時(shí)間: 2025-3-29 00:56
Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertaintys. The predominant method to generating uncertainty scores is to utilize a Bayesian formulation of deep learning. In this paper, we present a plug-and-play method to obtain samples from an already optimized model. Specifically, we present a simple, albeit principled methodology, to generate a number作者: OVER 時(shí)間: 2025-3-29 03:49
Calibration of Deep Medical Image Classifiers: An Empirical Comparison Using Dermatology and Histopa Mis-calibration is the deviation between predictive probability (confidence) and classification correctness. Well-calibrated classifiers enable cost-sensitive and selective decision-making. This paper presents an empirical investigation of calibration methods on two medical image datasets (multi-cl作者: chronology 時(shí)間: 2025-3-29 08:57 作者: Blood-Clot 時(shí)間: 2025-3-29 12:08
Generalized Probabilistic U-Net for?Medical Image Segementationas the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distributio作者: 流利圓滑 時(shí)間: 2025-3-29 17:34
Joint Paraspinal Muscle Segmentation and Inter-rater Labeling Variability Prediction with Multi-taskrly understood musculoskeletal disorder in adults. Accurate paraspinal muscle segmentation from MRI is crucial to enable new image-based biomarkers for the diagnosis and prognosis of LBP. Manual segmentation is laborious and time-consuming. In addition, high individual anatomical variations also pos作者: 窒息 時(shí)間: 2025-3-29 23:03
Information Gain Sampling for?Active Learning in?Medical Image Classificationing large datasets. Unlabelled datasets are easier to obtain, and in many contexts, it would be feasible for an expert to provide labels for a small subset of images. This work presents an information-theoretic active learning framework that guides the optimal selection of images from the unlabelled作者: 起來了 時(shí)間: 2025-3-30 01:59 作者: 節(jié)省 時(shí)間: 2025-3-30 07:47 作者: 晚間 時(shí)間: 2025-3-30 09:13
Dimitri Hamzaoui,Sarah Montagne,Rapha?le Renard-Penna,Nicholas Ayache,Hervé Delingettergestellt.Includes supplementary material: .Die ungeheure Menge an Faktenwissen, das die Biologie heutzutage vorweist, erlaubt es kaum noch jemandem, einen Blick zurück auf die Wissenschaftsgeschichte zu werfen. Und doch bietet ein solcher Rückblick ein spannendes Erlebnis. Von den Philosophen des a作者: Evolve 時(shí)間: 2025-3-30 16:01 作者: Frenetic 時(shí)間: 2025-3-30 18:50
