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Article
Computer Science, Artificial Intelligence
Hongyu Wang et al.
Summary: In this paper, a novel rib segmentation framework based on Unpaired Sample Augmentation and Multi-Scale Network is proposed to improve the accuracy of ribs segmentation with limited labeled samples. The algorithm learns pneumonia-related texture changes and generates augmented samples. The deep separation module and comprehensive loss function achieve refined segmentation results for each organ. Experimental results demonstrate that the proposed algorithm outperforms other methods in the segmentation performance of overlapping and fuzzy regions of multiple organs.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Bo Ni et al.
Summary: This paper proposes a novel deformable contour model for segmenting ultrasound image sequences. The model utilizes the power of deep learning network in learning image features to overcome the challenges in ultrasound image segmentation. Experimental results show that the proposed method outperforms state-of-the-art methods in clinical ultrasound images.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Engineering, Biomedical
Bin Yu et al.
Summary: Skin is the primary defense barrier of the human body, and skin cancer is a common type of cancer. Accurate skin lesion image segmentation is crucial for diagnosis and treatment planning. The proposed mCA-Net network significantly improves segmentation performance by introducing a new multi-scale channel attention module and other enhancements.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION
(2022)
Article
Dermatology
Tanja B. Jutzi et al.
Summary: The increasing incidence of malignant melanoma has led to advancements in early detection methods for skin cancer, such as visual inspection, dermoscopy, and whole-body photography combined with digital dermoscopy. However, some diagnostic techniques with high sensitivity may lack specificity, leading to potential false positives and unnecessary treatment. The use of artificial intelligence in skin cancer diagnostics shows promising results, but further research is needed to ensure their practical application in clinical settings.
AKTUELLE DERMATOLOGIE
(2022)
Proceedings Paper
Geosciences, Multidisciplinary
Tao Chen et al.
Summary: This paper addresses the issues of completeness and connectivity in automatic road extraction or segmentation. A novel dual-branch encoding block and context-guided filter block are proposed to improve accuracy. Experimental results show that the proposed method outperforms other methods on several metrics, with the improvement mainly coming from better integrity and connectivity of segmented roads.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Article
Engineering, Biomedical
Dehai Zhang et al.
Summary: This study proposes a pathological images analysis method based on multi-instance learning to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. The method includes a gated attention normalization mechanism, a bilinear attention multi-scale feature fusion mechanism, and a weighted loss function. Experimental results demonstrate excellent predictive performance of the method on multiple datasets.
IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE
(2022)
Review
Computer Science, Artificial Intelligence
Saeid Asgari Taghanaki et al.
Summary: This review categorizes leading deep learning-based medical and non-medical image segmentation solutions into six main groups and provides a comprehensive review of each group's contributions. It analyzes the limitations of current approaches and presents potential future research directions for improving semantic image segmentation.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Yanhan Zhang et al.
Summary: Researchers proposed a long-distance contextual attention network (LCA-Net) for skin disease image segmentation. The method achieved an average Jaccard index of 0.771 on the ISIC2017 dataset, representing a 0.6% improvement over the ISIC2017 Challenge Champion model. The average Jaccard index of 5-fold cross-validation on the ISIC2018 dataset is 0.8256. Experimental results show the proposed method has competitive performance compared to some advanced methods of image segmentation.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaomeng Li et al.
Summary: This article proposes a new semisupervised method for medical image segmentation, optimizing the network by introducing regularization loss and encouraging consistent predictions under different perturbations. The method is extensively validated on challenging medical image segmentation tasks, showing superior performance compared to state-of-the-art methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2021)
Proceedings Paper
Engineering, Biomedical
Anindo Saha et al.
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020)
(2020)
Proceedings Paper
Engineering, Biomedical
Mina Jafari et al.
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020)
(2020)
Article
Computer Science, Information Systems
Yading Yuan et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2019)
Article
Computer Science, Information Systems
Yu Zhao et al.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2019)
Article
Computer Science, Interdisciplinary Applications
Zaiwang Gu et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2019)
Article
Computer Science, Interdisciplinary Applications
Jose Luis Garcia-Arroyo et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2019)
Article
Chemistry, Analytical
Yuexiang Li et al.
Article
Computer Science, Interdisciplinary Applications
Yading Yuan et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2017)
