[HTML][HTML] A review: Deep learning for medical image segmentation using multi-modality fusion
Multi-modality is widely used in medical imaging, because it can provide multiinformation
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …
about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing …
[HTML][HTML] The promise of artificial intelligence and deep learning in PET and SPECT imaging
This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …
Latent correlation representation learning for brain tumor segmentation with missing MRI modalities
Magnetic Resonance Imaging (MRI) is a widely used imaging technique to assess brain
tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics …
tumor. Accurately segmenting brain tumor from MR images is the key to clinical diagnostics …
Multimodal spatial attention module for targeting multimodal PET-CT lung tumor segmentation
Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely
in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of …
in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of …
Using deep learning techniques in medical imaging: a systematic review of applications on CT and PET
I Domingues, G Pereira, P Martins, H Duarte… - Artificial Intelligence …, 2020 - Springer
Medical imaging is a rich source of invaluable information necessary for clinical judgements.
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
However, the analysis of those exams is not a trivial assignment. In recent times, the use of …
Deep multi-scale mesh feature learning for automated labeling of raw dental surfaces from 3D intraoral scanners
Precisely labeling teeth on digitalized 3D dental surface models is the precondition for tooth
position rearrangements in orthodontic treatment planning. However, it is a challenging task …
position rearrangements in orthodontic treatment planning. However, it is a challenging task …
Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques
This study presents a comprehensive systematic review focusing on the applications of deep
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 …
A three-way density peak clustering method based on evidence theory
Density peaks clustering (DPC) algorithm is an efficient and simple clustering method
attracting the attention of many researchers. However, its strategy of assigning each non …
attracting the attention of many researchers. However, its strategy of assigning each non …
[HTML][HTML] Representing uncertainty and imprecision in machine learning: A survey on belief functions
Z Liu, S Letchmunan - Journal of King Saud University-Computer and …, 2024 - Elsevier
Uncertainty and imprecision accompany the world we live in and occur in almost every
event. How to better interpret and manage uncertainty and imprecision play a vital role in …
event. How to better interpret and manage uncertainty and imprecision play a vital role in …
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 …