A deep image classification model based on prior feature knowledge embedding and application in medical diagnosis.

Aiming at the problem of image classification with insignificant morphological structural features, strong target correlation, and low signal-to-noise ratio, combined with prior feature knowledge embedding, a deep learning method based on ResNet and Radial Basis Probabilistic Neural Network (RBPNN) is proposed model. Taking ResNet50 as a visual modeling network, it uses feature pyramid and self-attention mechanism to extract appearance and semantic features of images at multiple scales, and associate and enhance local and global features. Taking into account the diversity of category features, channel cosine similarity attention and dynamic C-means clustering algorithms are used to select representative sample features in different category of sample subsets to implicitly express prior category feature knowledge, and use them as the kernel centers of radial basis probability neurons (RBPN) to realize the embedding of diverse prior feature knowledge.

What do macroinvertebrate indices measure? Stressor-specific stream macroinvertebrate indices can be confounded by other stressors.

Monitoring programmes worldwide use biota to assess the "health" of water bodies. Indices based on biota are used to describe the change in status of sites over time, to identify progress against management targets and to diagnose the causes of biological degradation. A variety of numerical stressor-specific biotic indices have been developed based on the response of biota to differences in stressors among sites.

Parental and health visitor perceptions on growth screening in early childhood: a qualitative study.

Background: Growth screening in early childhood can help identify children with a range of medical and psychosocial vulnerabilities. In the UK, childhood growth and development up to age 5 years are assessed through the Healthy Child Programme, delivered by health visitors. However, formal criteria to trigger referrals for onward investigation are unclear.