Gaining access to large, labelled sets of relevant images is crucial for the development and testing of biomedical imaging algorithms. Using images found in biomedical research articles would contribute some way towards a solution to this problem. However, this approach critically depends on being able to identify the most relevant images from very large sets of potentially useful figures. In this paper a deep convolutional neural network (CNN) classifier is trained using only synthetic data, to rapidly and accurately label raw images taken from biomedical articles. We apply this method in the context of detecting faces in biomedical images; and show that the classifier is able to retrieve figures containing faces with an average precision ...
Localization of region of interest (ROI) is paramount to the analysis of medical images to assist in...
This thesis proposes different models for a variety of applications, such as semantic segmentation, ...
In recent years, deep learning has provided the breakthrough of many new practical applications of m...
Gaining access to large, labelled sets of relevant images is crucial for the development and testing...
This paper shows promising results in the application of Convolutional Neural Networks (CNN) to biom...
Scientific figures contain significant amounts of information but present different challenges relat...
The classification of medical images and illustrations from the biomedical literature is important f...
Background and objectives: Highly accurate classification of biomedical images is an essential task ...
Deep learning models are more often used in the medical field as a result of the rapid development o...
Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to na...
The tremendous success of machine learning algorithms at image recognition tasks in recent years int...
Compound figure detection on figures and associated captions is the first step to making medical fig...
Much of medical knowledge is stored in the biomedical literature, collected in archives like PubMed ...
Cancer is a serious disease that causes death by genomic disorder combination and diversity of unrea...
Breast Cancer is a serious threat and one of the largest causes of death of women throughout the wor...
Localization of region of interest (ROI) is paramount to the analysis of medical images to assist in...
This thesis proposes different models for a variety of applications, such as semantic segmentation, ...
In recent years, deep learning has provided the breakthrough of many new practical applications of m...
Gaining access to large, labelled sets of relevant images is crucial for the development and testing...
This paper shows promising results in the application of Convolutional Neural Networks (CNN) to biom...
Scientific figures contain significant amounts of information but present different challenges relat...
The classification of medical images and illustrations from the biomedical literature is important f...
Background and objectives: Highly accurate classification of biomedical images is an essential task ...
Deep learning models are more often used in the medical field as a result of the rapid development o...
Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to na...
The tremendous success of machine learning algorithms at image recognition tasks in recent years int...
Compound figure detection on figures and associated captions is the first step to making medical fig...
Much of medical knowledge is stored in the biomedical literature, collected in archives like PubMed ...
Cancer is a serious disease that causes death by genomic disorder combination and diversity of unrea...
Breast Cancer is a serious threat and one of the largest causes of death of women throughout the wor...
Localization of region of interest (ROI) is paramount to the analysis of medical images to assist in...
This thesis proposes different models for a variety of applications, such as semantic segmentation, ...
In recent years, deep learning has provided the breakthrough of many new practical applications of m...