Bayesian Active LearningBias-corrected uncertaintyDropweightsImage annotationSemantic segmentationclassificationDeep Learning has achieved a state-of-the-art performance in medical imaging analysis but requires a large number of labelled images to obtain good adequate performance. However, such labelled images ...
Deep Learning methods are acknowledged as the state-of-art of many computer vision tasks such as image classification and segmentation (He et al., 2015, 2016; LeCun et al., 1998; Metcalf et al., 2019). More recently, Deep Learning architectures are being widely used to perform regression ...
from single-molecule motion in living-cells to movement ecology. The quest is to decipher the physical mechanisms encoded in the data and thus to better understand the probed systems. We here augment recently proposed machine-learning techniques for decoding anomalous...
As machine learning algorithms become more and more accurate at a variety of tasks, their inner workings become harder and harder to understand. The trend will make it increasingly difficult to avoid feature 1 of the WMD taxonomy. Current advanced techniques like deep learning are creating models ...
SegNet: a deep convolutional encoder-decoder architecture for image segmentation IEEE Trans Pattern Anal Mach Intell, 39 (12) (2017), pp. 2481-2495 View in ScopusGoogle Scholar [42] M.C. Krygier, T. LaBonte, C. Martinez, C. Norris, K. Sharma, L.N. Collins, et al. Quantifying the...
We also observed that the uncertainty estimates were inversely correlated with the model performance, underlying its utility for highlighting areas where manual inspection/correction might be needed. 展开 关键词: deep learning image segmentation retinal imaging optical coherence tomography uncertainty ...
Amodal segmentation is a relatively new research direction, but research on robustness to partial occlusion has received a lot of attention. In the following we focus solely on works that directly relate to ours. Recent studies [21, 40] showed that t...
Sign in to download hi-res image Fig. 4. Main supervised learning algorithms developed in Julia. Bayesian model There are two key points in the definition of Bayesian model: independence between features and the Bayesian theorem. One of the most important research areas of Bayesian model is Baye...
In this paper, we propose a method for retinal layer segmentation and quantification of uncertainty based on Bayesian deep learning. Our method not only performs end-to-end segmentation of retinal layers, but also gives the pixel wise uncertainty measure of the segmentation output. The generated ...
For additional information on BMA, particularly its theoretical properties, we suggest referring to [23,24]. Read more View article Related terms: Support Vector Machine Image Segmentation Machine Learning Deep Learning Decision Trees Bayesian Networks Convolutional Neural Network Random Decision Forest Neur...