COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentation of infected areas from computed tomography (CT) images can be used to assist and provide useful information for COVID-19 diagnosis. Although several deep learning-based segmentation methods have been proposed for COVID-19 segmentation and have achieved state-of-the-art results, the segmentation accuracy is still not high enough (approximately 85%) due to the variations of COVID-19 infected areas (such as shape and size variations) and the similarities between COVID-19 and non-COVID-infected areas. To improve the segmentation accuracy of COVID-19 infected areas, we propose an interactive attention refinement network (Attention RefNet). The interactive attention refinement network can be connected with any segmentation network and trained with the segmentation network in an end-to-end fashion. We propose a skip connection attention module to improve the important features in both segmentation and refinement networks and a seed point module to enhance the important seeds (positions) for interactive refinement. The effectiveness of the proposed method was demonstrated on public datasets (COVID-19CTSeg and MICCAI) and our private multicenter dataset. The segmentation accuracy was improved to more than 90%. We also confirmed the generalizability of the proposed network on our multicenter dataset. The proposed method can still achieve high segmentation accuracy.

Mitosis detection is one of the challenging steps in biomedical imaging research, which can be used to observe the cell behavior. Most of the already existing methods that are applied in detecting mitosis usually contain many nonmitotic events (normal cell and background) in the result (false positives, FPs). In order to address such problem, in this study, we propose to apply 2.5-dimensional (2.5D) networks called CasDetNet_CLSTM, which can accurately detect mitotic events in 4D microscopic images. This CasDetNet_CLSTM involves a 2.5D faster region-based convolutional neural network (Faster R-CNN) as the first network, and a convolutional long short-term memory (CLSTM) network as the second network. The first network is used to select candidate cells using the information from nearby slices, whereas the second network uses temporal information to eliminate FPs and refine the result of the first network. Our experiment shows that the precision and recall of our networks yield better results than those of other state-of-the-art methods.

The success of large-scale deep learning models in remote sensing tasks has been transformative, enabling significant advances in image classification, object detection, and image–text retrieval. However, their computational and memory demands pose challenges for deployment in resource-constrained environments. Knowledge distillation (KD) alleviates these issues by transferring knowledge from a strong teacher to a student model, which can be compact for efficient deployment or architecturally matched to improve accuracy under the same inference budget. In this paper, we introduce Hierarchical Multi-Segment Knowledge Distillation (HIMS_KD), a multi-stage framework that sequentially distills knowledge from a teacher into multiple assistant models specialized in low-, mid-, and high-level representations, and then aggregates their knowledge into the final student. We integrate feature-level alignment, auxiliary similarity-logit alignment, and supervised loss during distillation. Experiments on benchmark remote sensing datasets (RSITMD and RSICD) show that HIMS_KD improves retrieval performance and enhances zero-shot classification; and when a compact student is used, it reduces deployment cost while retaining strong accuracy.

This study investigates the effect of bases NaOH and KOH on okara, the soybean residue, in conventional pulping, based on 136 pulping conditions used as a dataset for random forest regression and gradient boosting predictive models. Okara CMC was formed and identified using Fourier-transform infrared spectroscopy (FTIR) to demonstrate a wide range of applications comparable to commercial CMC, with a low degree of substitution. The quality of okara pulp after basic pulping was analyzed based on the extracted cellulose yield and remaining protein content. The optimized pulping condition was a mixture of NaOH and KOH at a 30% concentration, resulting in an extracted cellulose yield of 24.5 wt% and a remaining protein content of 25.1%. The obtained okara pulp was converted into okara CMC with a controllable degree of substitution. The implemented dataset was used to train two predictive models: random forest regression and gradient boosting, to forecast key parameters for pulping (NaOH, KOH, AQ, and H₂O). Both models demonstrated excellent prediction performance, with R² values of 0.94 and 0.89, respectively, and showed similar residuals and predicted values. The close clustering of residuals around zero, along with the sharp and narrow curves observed, indicates that both the random forest and gradient boosting models provide precise and reliable predictions. The localized deviations observed in the residuals suggest that these models effectively capture detailed patterns in the data, leading to minimized prediction errors within specific ranges.