AIMC Topic: Semantics

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An Evaluation of Pretrained BERT Models for Comparing Semantic Similarity Across Unstructured Clinical Trial Texts.

Studies in health technology and informatics
Processing unstructured clinical texts is often necessary to support certain tasks in biomedicine, such as matching patients to clinical trials. Among other methods, domain-specific language models have been built to utilize free-text information. Th...

PBDiff: Neural network based program-wide diffing method for binaries.

Mathematical biosciences and engineering : MBE
Program-wide binary code diffing is widely used in the binary analysis field, such as vulnerability detection. Mature tools, including BinDiff and TurboDiff, make program-wide diffing using rigorous comparison basis that varies across versions, optim...

[What worries people with multiple sclerosis in Russia? Semantic analysis of patient messages using artificial intelligence tools].

Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova
OBJECTIVE: To study the needs of patients suffering from multiple sclerosis (MS) in Russia.

Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection.

Journal of X-ray science and technology
BACKGROUND: Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster than PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models.

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
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
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)
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
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
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
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...