Evaluating a clinical mentorship intervention on maternal and neonatal complications in primary health facilities in Blantyre district, Malawi: A longitudinal analysis of the Global Action in Nursing program.

Background: While Malawi has made great strides increasing the number of facility-based births, maternal and neonatal mortality remains high. An intervention started in 2019 provided short-course training followed by year-long longitudinal bedside mentorship for nurse midwives at seven health facilities in Blantyre district. The intervention was initiated following invitation from the district to improve outcomes for patients during childbirth.

Texture-based Error Analysis for Image Super-Resolution.

Evaluation practices for image super-resolution (SR) use a single-value metric, the PSNR or SSIM, to determine model performance. This provides little insight into the source of errors and model behavior. Therefore, it is beneficial to move beyond the conventional approach and reconceptualize evaluation with interpretability as our main priority.

Dynamic High-Pass Filtering and Multi-Spectral Attention for Image Super-Resolution.

Deep convolutional neural networks (CNNs) have pushed forward the frontier of super-resolution (SR) research. However, current CNN models exhibit a major flaw: they are biased towards learning low-frequency signals. This bias becomes more problematic for the image SR task which targets reconstructing all fine details and image textures.

Channel Embedding for Informative Protein Identification from Highly Multiplexed Images.

Interest is growing rapidly in using deep learning to classify biomedical images, and interpreting these deep-learned models is necessary for life-critical decisions and scientific discovery. Effective interpretation techniques accelerate biomarker discovery and provide new insights into the etiology, diagnosis, and treatment of disease. Most interpretation techniques aim to discover spatially-salient regions within images, but few techniques consider imagery with multiple channels of information.