[HTML][HTML] A review of uncertainty estimation and its application in medical imaging
The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …
importance. Deep learning has shown great promise in medical imaging, but the reliability …
Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey
The rapid development of artificial intelligence has constantly reshaped the field of
intelligent healthcare and medicine. As a vital technology, multimodal learning has …
intelligent healthcare and medicine. As a vital technology, multimodal learning has …
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation
Single-modality medical images generally do not contain enough information to reach an
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …
accurate and reliable diagnosis. For this reason, physicians commonly rely on multimodal …
Application of belief functions to medical image segmentation: A review
The investigation of uncertainty is of major importance in risk-critical applications, such as
medical image segmentation. Belief function theory, a formal framework for uncertainty …
medical image segmentation. Belief function theory, a formal framework for uncertainty …
Learning feature fusion via an interpretation method for tumor segmentation on PET/CT
Accurate tumor segmentation of multi-modality PET/CT images plays a vital role in computer-
aided cancer diagnosis and treatment. It is crucial to rationally fuse the complementary …
aided cancer diagnosis and treatment. It is crucial to rationally fuse the complementary …
Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI
Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the
complimentary information within sequences. However, redundant information exists across …
complimentary information within sequences. However, redundant information exists across …
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 …
Generative adversarial network for trimodal medical image fusion using primitive relationship reasoning
J Huang, X Li, H Tan, X Cheng - IEEE Journal of Biomedical …, 2024 - ieeexplore.ieee.org
Medical image fusion has become a hot biomedical image processing technology in recent
years. The technology coalesces useful information from different modal medical images …
years. The technology coalesces useful information from different modal medical images …
Analysis of Multimodality Fusion of Medical Image Segmentation Employing Deep Learning
Medical imaging methods using multiple modalities are used more frequently in both clinical
settings and academic studies. The use of ensemble learning and associated multimodal …
settings and academic studies. The use of ensemble learning and associated multimodal …
Deep evidential remote sensing landslide image classification with a new divergence, multi-scale saliency and an improved three-branched fusion
J Zhang, Q Cui, X Ma - IEEE Journal of Selected Topics in …, 2024 - ieeexplore.ieee.org
Hitherto, image-level classification on remote sensing landslide images has been paid
attention to, but the accuracy of traditional deep learning-based methods still have room for …
attention to, but the accuracy of traditional deep learning-based methods still have room for …