Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
[HTML][HTML] Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …
respect to the quantity of high-performing solutions reported in the literature. End users are …
[HTML][HTML] Snow depth estimation at country-scale with high spatial and temporal resolution
Monitoring snow depth is important for applications such as hydrology, energy planning,
ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate …
ecology, and safety evaluation for outdoor winter activities. Most methods able to estimate …
[HTML][HTML] Automatic uncertainty-based quality controlled T1 map** and ECV analysis from native and post-contrast cardiac T1 map** images using Bayesian vision …
Deep learning-based methods for cardiac MR segmentation have achieved state-of-the-art
results. However, these methods can generate incorrect segmentation results which can …
results. However, these methods can generate incorrect segmentation results which can …
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …
Uncertainty estimation for safety-critical scene segmentation via fine-grained reward maximization
H Yang, C Chen, Y Chen, HC Yip… - Advances in Neural …, 2023 - proceedings.neurips.cc
Uncertainty estimation plays an important role for future reliable deployment of deep
segmentation models in safety-critical scenarios such as medical applications. However …
segmentation models in safety-critical scenarios such as medical applications. However …
[HTML][HTML] Comparative benchmarking of failure detection methods in medical image segmentation: unveiling the role of confidence aggregation
Semantic segmentation is an essential component of medical image analysis research, with
recent deep learning algorithms offering out-of-the-box applicability across diverse datasets …
recent deep learning algorithms offering out-of-the-box applicability across diverse datasets …
Stochastic uncertainty quantification techniques fail to account for inter-analyst variability in white matter hyperintensity segmentation
B Philps, M del C. Valdes Hernandez… - Annual Conference on …, 2024 - Springer
Abstract White Matter Hyperintensities (WMH) are important neuroradiological markers of
small vessel disease in brain MRI, with automatic segmentation tasks essential in research …
small vessel disease in brain MRI, with automatic segmentation tasks essential in research …
Can input reconstruction be used to directly estimate uncertainty of a regression U-Net model?--Application to proton therapy dose prediction for head and neck …
Estimating the uncertainty of deep learning models in a reliable and efficient way has
remained an open problem, where many different solutions have been proposed in the …
remained an open problem, where many different solutions have been proposed in the …
Can input reconstruction be used to directly estimate uncertainty of a dose prediction U‐Net model?
Background The reliable and efficient estimation of uncertainty in artificial intelligence (AI)
models poses an ongoing challenge in many fields such as radiation therapy. AI models are …
models poses an ongoing challenge in many fields such as radiation therapy. AI models are …