[图书][B] Deep neural networks for multimodal imaging and biomedical applications
A Suresh, R Udendhran, S Vimal - 2020 - books.google.com
The field of healthcare is seeing a rapid expansion of technological advancement within
current medical practices. The implementation of technologies including neural networks …
current medical practices. The implementation of technologies including neural networks …
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of medical image
analysis. However, despite these achievements, the further enhancement of deep learning …
analysis. However, despite these achievements, the further enhancement of deep learning …
Mitigating calibration bias without fixed attribute grouping for improved fairness in medical imaging analysis
Trustworthy deployment of deep learning medical imaging models into real-world clinical
practice requires that they be calibrated. However, models that are well calibrated overall …
practice requires that they be calibrated. However, models that are well calibrated overall …
[HTML][HTML] A holistic overview of deep learning approach in medical imaging
Medical images are a rich source of invaluable necessary information used by clinicians.
Recent technologies have introduced many advancements for exploiting the most of this …
Recent technologies have introduced many advancements for exploiting the most of this …
[HTML][HTML] Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
Advancements in deep learning techniques carry the potential to make significant
contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis …
contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis …
[HTML][HTML] On the role of artificial intelligence in medical imaging of COVID-19
Although a plethora of research articles on AI methods on COVID-19 medical imaging are
published, their clinical value remains unclear. We conducted the largest systematic review …
published, their clinical value remains unclear. We conducted the largest systematic review …
[HTML][HTML] Medical deep learning—A systematic meta-review
Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …
few years. For example, in image processing and analysis, deep learning algorithms were …
[HTML][HTML] Deep Learning Approaches for Medical Image Analysis and Diagnosis
In addition to enhancing diagnostic accuracy, deep learning techniques offer the potential to
streamline workflows, reduce interpretation time, and ultimately improve patient outcomes …
streamline workflows, reduce interpretation time, and ultimately improve patient outcomes …
Towards general purpose vision foundation models for medical image analysis: An experimental study of DINOv2 on radiology benchmarks
The integration of deep learning systems into the medical domain has been hindered by the
resource-intensive process of data annotation and the inability of these systems to …
resource-intensive process of data annotation and the inability of these systems to …
Data diversity and virtual imaging in AI-based diagnosis: A case study based on COVID-19
FI Tushar, L Dahal, S Sotoudeh-Paima, E Abadi… - arXiv preprint arXiv …, 2023 - arxiv.org
Many studies have investigated deep-learning-based artificial intelligence (AI) models for
medical imaging diagnosis of the novel coronavirus (COVID-19), with many reports of near …
medical imaging diagnosis of the novel coronavirus (COVID-19), with many reports of near …