Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Ihler, Sontje"'
Computer aided diagnosis (CAD) has gained an increased amount of attention in the general research community over the last years as an example of a typical limited data application - with experiments on labeled 100k-200k datasets. Although these data
Externí odkaz:
http://arxiv.org/abs/2302.06684
Publikováno v:
In SLAS Technology August 2024 29(4)
Autor:
Ihler Sontje, Kuhnke Felix
Publikováno v:
Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 658-661 (2023)
The AUC margin loss is a valuable loss function for medical image classification as it addresses the problems of imbalanced and noisy labels. It is used by the current winner of the CheXpert competition. The CheXpert dataset is a large dataset (200k+
Externí odkaz:
https://doaj.org/article/87bfb59aa10946d0b126813535ad1f87
The consideration of predictive uncertainty in medical imaging with deep learning is of utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by variational Bayesian inference with Monte Carlo dropout to regression tasks
Externí odkaz:
http://arxiv.org/abs/2104.12376
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer
Fast motion feedback is crucial in computer-aided surgery (CAS) on moving tissue. Image-assistance in safety-critical vision applications requires a dense tracking of tissue motion. This can be done using optical flow (OF). Accurate motion prediction
Externí odkaz:
http://arxiv.org/abs/2007.04928
The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. In this paper, different logit scaling methods are extended to dropout variational inference to recalibrate model uncertainty. Expec
Externí odkaz:
http://arxiv.org/abs/2006.11584
Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. The uncertainty does not represent the model error well. In this paper, temperature scaling is extended to dropout variational inference
Externí odkaz:
http://arxiv.org/abs/1909.13550
We evaluate two different methods for the integration of prediction uncertainty into diagnostic image classifiers to increase patient safety in deep learning. In the first method, Monte Carlo sampling is applied with dropout at test time to get a pos
Externí odkaz:
http://arxiv.org/abs/1908.00792
We present deformable unsupervised medical image registration using a randomly-initialized deep convolutional neural network (CNN) as regularization prior. Conventional registration methods predict a transformation by minimizing dissimilarities betwe
Externí odkaz:
http://arxiv.org/abs/1908.00788
According to the World Health Organization, 285 million people worldwide live with visual impairment. The most commonly used imaging technique for diagnosis in ophthalmology is optical coherence tomography (OCT). However, analysis of retinal OCT requ
Externí odkaz:
http://arxiv.org/abs/1904.00790