Leveraging shortest dependency paths in low-resource biomedical relation extraction.

Background: Biomedical Relation Extraction (RE) is essential for uncovering complex relationships between biomedical entities within text. However, training RE classifiers is challenging in low-resource biomedical applications with few labeled examples.

Methods: We explore the potential of Shortest Dependency Paths (SDPs) to aid biomedical RE, especially in situations with limited labeled examples.

MERIT: inimal Suprvision Through Label Augmentation for Biomedical Exraction.

Relation Extraction (RE) is an important task in extracting structured data from free biomedical text. Obtaining labeled data needed to train RE models in specialized domains such as biomedicine can be very expensive because it requires expert knowledge. Thus, it is often the case that RE models need to be trained from relatively small labeled data sets.

QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network.

Tumor segmentation in brain MRI images is a noted process that can make the tumor easier to diagnose and lead to effective radiotherapy planning. Providing and building intelligent medical systems can be considered as an aid for physicians. In many cases, the presented methods' reliability is at a high level, and such systems are used directly.