AIMC Topic:
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Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2022
BACKGROUND: Breast cancer has long been one of the major global life-threatening illnesses among women. Surgery and adjuvant therapy, coupled with early detection, could save many lives. This underscores the importance of mammography, a cost-effectiv...
Robust Engineering-based Unified Biomedical Imaging Framework for Liver Tumor Segmentation.
Current medical imaging
Jan 1, 2022
BACKGROUND: Computer vision in general and semantic segmentation has experienced many achievements in recent years. Consequently, the emergence of medical imaging has provided new opportunities for conducting artificial intelligence research. Since c...
BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions.
Bioinformatics (Oxford, England)
Dec 11, 2021
MOTIVATION: Predicting entity relationship can greatly benefit important biomedical problems. Recently, a large amount of biomedical heterogeneous networks (BioHNs) are generated and offer opportunities for developing network-based learning approache...
Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning.
Journal of the American Medical Informatics Association : JAMIA
Nov 25, 2021
OBJECTIVE: There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem o...
Road crack segmentation using an attention residual U-Net with generative adversarial learning.
Mathematical biosciences and engineering : MBE
Nov 4, 2021
This paper proposed an end-to-end road crack segmentation model based on attention mechanism and deep FCN with generative adversarial learning. We create a segmentation network by introducing a visual attention mechanism and residual module to a full...
Generative Image Inpainting for Retinal Images using Generative Adversarial Networks.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nov 1, 2021
The diagnosis and treatment of eye diseases is heavily reliant on the availability of retinal imagining equipment. To increase accessibility, lower-cost ophthalmoscopes, such as the Arclight, have been developed. However, a common drawback of these d...
Surgical instrument segmentation based on multi-scale and multi-level feature network.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nov 1, 2021
Surgical instrument segmentation is critical for the field of computer-aided surgery system. Most of deep-learning based algorithms only use either multi-scale information or multi-level information, which may lead to ambiguity of semantic informatio...
Understanding the Gap Between Information Models and Realism-Based Ontologies Using the Generic Component Model.
Studies in health technology and informatics
Oct 27, 2021
The wide-spread use of Common Data Models and information models in biomedical informatics encourages assumptions that those models could provide the entirety of what is needed for knowledge representation purposes. Based on the lack of computable se...
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information.
Briefings in bioinformatics
Sep 2, 2021
Recently, language representation models have drawn a lot of attention in the natural language processing field due to their remarkable results. Among them, bidirectional encoder representations from transformers (BERT) has proven to be a simple, yet...
Why can deep convolutional neural networks improve protein fold recognition? A visual explanation by interpretation.
Briefings in bioinformatics
Sep 2, 2021
As an essential task in protein structure and function prediction, protein fold recognition has attracted increasing attention. The majority of the existing machine learning-based protein fold recognition approaches strongly rely on handcrafted featu...
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