AI Medical Compendium Topic

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Semantics

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CLDTLog: System Log Anomaly Detection Method Based on Contrastive Learning and Dual Objective Tasks.

Sensors (Basel, Switzerland)
System logs are a crucial component of system maintainability, as they record the status of the system and essential events for troubleshooting and maintenance when necessary. Therefore, anomaly detection of system logs is crucial. Recent research ha...

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.

Scientific reports
In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs o...

From jeopardy champion to drug discovery; semantic similarity artificial intelligence.

Autophagy
We have employed artificial intelligence to streamline the small molecule drug screening pipeline and identified the cholesterol-reducing compound probucol in the process. Probucol augmented mitophagy and prevented loss of dopaminergic neurons in fli...

Block-level dependency syntax based model for end-to-end aspect-based sentiment analysis.

Neural networks : the official journal of the International Neural Network Society
End-to-End aspect-based sentiment analysis (E2E-ABSA) aims to jointly extract aspect terms and identify their sentiment polarities. Although previous research has demonstrated that syntax knowledge can be beneficial for E2E-ABSA, standard syntax depe...

The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers.

Psychological methods
The language that individuals use for expressing themselves contains rich psychological information. Recent significant advances in Natural Language Processing (NLP) and Deep Learning (DL), namely transformers, have resulted in large performance gain...

Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations. In this paper, we exploit additional evidence by also making use of ....

Explainable hybrid word representations for sentiment analysis of financial news.

Neural networks : the official journal of the International Neural Network Society
Due to the increasing interest of people in the stock and financial market, the sentiment analysis of news and texts related to the sector is of utmost importance. This helps the potential investors in deciding what company to invest in and what are ...

A Large-scale Synthetic Pathological Dataset for Deep Learning-enabled Segmentation of Breast Cancer.

Scientific data
The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive anno...

BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation.

Computers in biology and medicine
Medical image segmentation enables doctors to observe lesion regions better and make accurate diagnostic decisions. Single-branch models such as U-Net have achieved great progress in this field. However, the complementary local and global pathologica...

Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation.

Scientific reports
Medical image segmentation provides various effective methods for accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, and thus fusing rich m...