[HTML][HTML] Multimodal machine learning in precision health: A scoping review
Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …
sector including utilization for clinical decision-support. Its use has historically been focused …
Deep learning in medical image registration: a review
This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …
methods. We summarized the latest developments and applications of DL-based registration …
AI in medical imaging informatics: current challenges and future directions
This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …
imaging informatics, discusses clinical translation, and provides future directions for …
An explainable machine learning model for early detection of Parkinson's disease using LIME on DaTSCAN imagery
Parkinson's Disease (PD) is a degenerative and progressive neurological condition. Early
diagnosis can improve treatment for patients and is performed through dopaminergic …
diagnosis can improve treatment for patients and is performed through dopaminergic …
Advances in machine learning modeling reviewing hybrid and ensemble methods
The conventional machine learning (ML) algorithms are continuously advancing and
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …
evolving at a fast-paced by introducing the novel learning algorithms. ML models are …
[HTML][HTML] Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey
Management of brain tumors is based on clinical and radiological information with
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of …
An intelligent diagnosis method of brain MRI tumor segmentation using deep convolutional neural network and SVM algorithm
W Wu, D Li, J Du, X Gao, W Gu, F Zhao… - … methods in medicine, 2020 - Wiley Online Library
Among the currently proposed brain segmentation methods, brain tumor segmentation
methods based on traditional image processing and machine learning are not ideal enough …
methods based on traditional image processing and machine learning are not ideal enough …
Intelligent systems using triboelectric, piezoelectric, and pyroelectric nanogenerators
Recent advances in artificial intelligence, computer science, communication, sensing and
actuation technologies have resulted in the development of several novel intelligent …
actuation technologies have resulted in the development of several novel intelligent …
Bone segmentation on whole-body CT using convolutional neural network with novel data augmentation techniques
Background The purpose of this study was to develop and evaluate an algorithm for bone
segmentation on whole-body CT using a convolutional neural network (CNN). Methods …
segmentation on whole-body CT using a convolutional neural network (CNN). Methods …
Machine-learning, MRI bone shape and important clinical outcomes in osteoarthritis: data from the Osteoarthritis Initiative
MA Bowes, K Kacena, OA Alabas, AD Brett… - Annals of the …, 2021 - ard.bmj.com
Objectives Osteoarthritis (OA) structural status is imperfectly classified using radiographic
assessment. Statistical shape modelling (SSM), a form of machine-learning, provides …
assessment. Statistical shape modelling (SSM), a form of machine-learning, provides …