BTFormer: Blast transformer for human blastocyst components segmentation.

Assessing embryo quality through segmentation of blastocyst components is crucial, as embryo morphology directly correlates with its potential for implantation. However, automatic blastocyst segmentation remains a challenging task due to factors such as poor contrast, noise, and ambiguous boundaries between different tissue structures. In this study, we introduce a novel transformer-based architecture, termed BTFormer (Blastocyst Transformer), designed to effectively segment blastocyst components.

SCKansformer: Fine-Grained Classification of Bone Marrow Cells via Kansformer Backbone and Hierarchical Attention Mechanisms.

The incidence and mortality rates of malignant tumors, such as acute leukemia, have risen significantly. Clinically, hospitals rely on cytological examination of peripheral blood and bone marrow smears to diagnose malignant tumors, with accurate blood cell counting being crucial. Existing automated methods face challenges such as low feature expression capability, poor interpretability, and redundant feature extraction when processing high-dimensional microimage data.

Semi-supervised point consistency network for retinal artery/vein classification.

Recent deep learning methods with convolutional neural networks (CNNs) have boosted advance prosperity of medical image analysis and expedited the automatic retinal artery/vein (A/V) classification. However, it is challenging for these CNN-based approaches in two aspects: (1) specific tubular structures and subtle variations in appearance, contrast, and geometry, which tend to be ignored in CNNs with network layer increasing; (2) limited well-labeled data for supervised segmentation of retinal vessels, which may hinder the effectiveness of deep learning methods. To address these issues, we propose a novel semi-supervised point consistency network (SPC-Net) for retinal A/V classification.

Residual grounding transformer network for terrain recognition on the lunar surface.

It is of paramount importance for a rover running on an extraterrestrial body surface to recognize the dangerous zones autonomously. This automation is inevitable due to the communication delay. However, as far as we know, there are few annotated terrain recognition datasets for extraterrestrial bodies.

18α-Glycyrrhetinic acid (GA) ameliorates fructose-induced nephropathy in mice by suppressing oxidative stress, dyslipidemia and inflammation.

Excessive fructose (FRU) intake can result in insulin resistance and metabolic disorder, which are related to renal injury.18α-Glycyrrhetinic acid (GA) is a bioactive component mainly extracted from Glycyrrhiza radix, and has anti-oxidant and anti-inflammatory activities. However, its effects on FRU-induced renal injury still remain unclear.