Journal of imaging informatics in medicine
Feb 16, 2024
Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consumin...
"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and d...
BMC medical informatics and decision making
Nov 16, 2023
BACKGROUND: A large collection of dialogues between patients and doctors must be annotated for medical named entities to build intelligence for telemedicine. However, since most patients involved in telemedicine deliver related named entities in info...
INTRODUCTION: Stem cell therapy has been gaining interest in the regeneration rather than repair of lost human tissues. However, the manual analysis of stem cells prior to implantation is a cumbersome task that can be automated to improve the efficie...
In medical imaging, deep learning models can be a critical tool to shorten time-to-diagnosis and support specialized medical staff in clinical decision making. The successful training of deep learning models usually requires large amounts of quality ...
Data curation is a bottleneck for many informatics pipelines. A specific example of this is aggregating data from preclinical studies to identify novel genetic pathways for atherosclerosis in humans. This requires extracting data from published mouse...
Oral surgery, oral medicine, oral pathology and oral radiology
Mar 7, 2023
OBJECTIVE: The present study aims to quantify clinicians' perceptions of oral potentially malignant disorders (OPMDs) when evaluating, classifying, and manually annotating clinical images, as well as to understand the source of inter-observer variabi...
Journal of integrative bioinformatics
Aug 26, 2022
Deep learning models achieve high-quality results in image processing. However, to robustly optimize parameters of deep neural networks, large annotated datasets are needed. Image annotation is often performed manually by experts without a comprehens...
International journal of neural systems
Jul 30, 2022
A practical problem in supervised deep learning for medical image segmentation is the lack of labeled data which is expensive and time-consuming to acquire. In contrast, there is a considerable amount of unlabeled data available in the clinic. To mak...
Although natural language processing (NLP) can rapidly extract disease labels from radiology reports to create datasets for deep learning models, this may be less accurate than having radiologists manually review the images. In this study, we compare...
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