Enhancing Weakly Supervised Semantic Segmentation with Multi-label Contrastive Learning and LLM Features Guidance.

Histopathological whole-slide image (WSI) segmentation is essential for precise tissue characterization in medical diagnostics. However, traditional approaches require labor-intensive pixel-level annotations. To this end, we study weakly supervised semantic segmentation (WSSS) which uses patch-level classification labels, reducing annotation efforts significantly.

Dynamic decoding and dual synthetic data for automatic correction of grammar in low-resource scenario.

Grammar error correction systems are pivotal in the field of natural language processing (NLP), with a primary focus on identifying and correcting the grammatical integrity of written text. This is crucial for both language learning and formal communication. Recently, neural machine translation (NMT) has emerged as a promising approach in high demand.

DFTNet: Dual-Path Feature Transfer Network for Weakly Supervised Medical Image Segmentation.

Medical image segmentation has long suffered from the problem of expensive labels. Acquiring pixel-level annotations is time-consuming, labor-intensive, and relies on extensive expert knowledge. Bounding box annotations, in contrast, are relatively easy to acquire.

DMCGNet: A Novel Network for Medical Image Segmentation With Dense Self-Mimic and Channel Grouping Mechanism.

Automatic Medical Image Segmentation (MIS) can assist doctors by reducing labor and providing a unified standard. Nowadays, approaches based on Deep Learning have become mainstream for MIS because of their ability of automatic feature extraction. However, due to the plain network design and targets variety in medical images, the semantic features can hardly be extracted adequately.

Antioxidant activity and in vitro digestibility of dialdehyde starches as influenced by their physical and structural properties.

Dialdehyde starches (DASs) with different aldehyde contents were prepared by periodate oxidation of corn starch, and their antioxidant activity and digestibility were studied and related to their structural characteristics, including morphology, relative crystallinity, average molecular weights, swelling power and solubility. The results revealed that DASs were effective antioxidants as revealed by the 2,2'-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity. A significant positive correlation (r>0.