ArticleScientific reports2024
Medical image segmentation with UNet-based multi-scale context fusion.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Artificial Intelligence-Based Histopathology Segmentation for Resource-Constrained Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- [Research on surgical instrument segmentation algorithm based on frequency-domain adaptive feature decomposition and visual Mamba].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- MTRFU-Net: a lung nodule segmentation model based on improved U-Net architecture with spatial-frequency fusion.BMC medical imaging · 2026Article
- Three-dimensional spatial representation method for semantic boundaries in digital outcrop models.Scientific reports · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Cross-modal dual-domain bi-direction feature interaction network for medical imaging semantic segmentation.Scientific reports · 2025Article
- Expanding the role of radiomics and artificial intelligence in the management of inflammatory bowel disease: Insights, opportunities, and challenges.World journal of gastroenterology · 2025Article
- Automated lumbar spine segmentation in MRI using an enhanced U-Net with inception module and dual-output mechanism.Scientific reports · 2025Article
- A deep learning-based dual-branch framework for automated skin lesion segmentation and classification via dermoscopic Images.Scientific reports · 2025Article
- Enhanced brain tumor segmentation in medical imaging using multi-modal multi-scale contextual aggregation and attention fusion.Scientific reports · 2025Article
- Efficient 3D Biomedical Image Segmentation by Parallelly Multiscale Transformer-CNN Aggregation Network.Chemical & biomedical imaging · 2025Article
- Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs.Scientific reports · 2025Article
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- Alternate encoder and dual decoder CNN-Transformer networks for medical image segmentation.Scientific reports · 2025Article
- CervSpineNet: a hybrid deep learning-based approach for the segmentation of cervical spinous processes.Frontiers in bioengineering and biotechnology · 2025Article
- Universal medical image segmentation via in-context cross-attention.Frontiers in artificial intelligence · 2025Article
- Utilizing Detectron2 for accurate and efficient colon cancer detection in histopathological images.Frontiers in bioengineering and biotechnology · 2025Article
- An Enhanced Deep Learning Model for Effective Crop Pest and Disease Detection.Journal of imaging · 2024Article
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2 authors.
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Abstract
Histopathological examination holds a crucial role in cancer grading and serves as a significant reference for devising individualized patient treatment plans in clinical practice. Nevertheless, the distinctive features of numerous histopathological image targets frequently contribute to suboptimal segmentation performance. In this paper, we propose a UNet-based multi-scale context fusion algorithm for medical image segmentation, which extracts rich contextual information by extracting semantic information at different encoding stages and assigns different weights to the semantic information at different scales through TBSFF module to improve the learning ability of the network for features. Through multi-scale context fusion and feature selection networks, richer semantic features and detailed information are extracted. The target can be more accurately segmented without significantly increasing the extra overhead. The results demonstrate that our algorithm achieves superior Dice and IoU scores with a relatively small parameter count. Specifically, on the GlaS dataset, the Dice score is 90.56, and IoU is 83.47. For the MoNuSeg dataset, the Dice score is 79.07, and IoU is 65.98.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.