ArticleHeliyon2023
PCF-Net: Position and context information fusion attention convolutional neural network for skin lesion segmentation.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Advancements and challenges of artificial intelligence in dermatology: a review of applications and perspectives in China.Frontiers in digital health · 2025Pooled it
- Enhancing skin lesion segmentation with a fusion of convolutional neural networks and transformer models.Heliyon · 2024Article
- Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin lesion segmentation is a crucial step in the process of skin cancer diagnosis and treatment. The variation in position, shape, size and edges of skin lesion areas poses a challenge for accurate segmentation of skin lesion areas through dermoscopic images. To meet these challenges, in this paper, using UNet as the baseline model, a convolutional neural network based on position and context information fusion attention is proposed, called PCF-Net. A novel two-branch attention mechanism is designed to aggregate Position and Context information, called Position and Context Information Aggregation Attention Module (PCFAM). A global context information complementary module (GCCM) was developed to obtain long-range dependencies. A multi-scale grouped dilated convolution feature extraction module (MSEM) was proposed to capture multi-scale feature information and place it in the bottleneck of UNet. On the ISIC2018 dataset, a large volume of ablation experiments demonstrated the superiority of PCF-Net for dermoscopic image segmentation after adding PCFAM, GCCM and MSEM. Compared with other state-of-the-art methods, the performance of PCF-Net achieves a competitive result in all metrics.
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Registered trials
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.