ArticleFrontiers in robotics and AI2020
Improving CT Image Tumor Segmentation Through Deep Supervision and Attentional Gates.
Article in Frontiers in robotics and AI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2023Trial
- Precision Through Detail: Radiomics and Windowing Techniques as Key for Detecting Dens Axis Fractures in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- External validation of deep learning-derived 18F-FDG PET/CT delta biomarkers for loco-regional control in head and neck cancer.Acta oncologica (Stockholm, Sweden) · 2025Article
- AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.Abdominal radiology (New York) · 2025Review
- Clinical Evaluation of Deep Learning for Tumor Delineation onJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images.Diagnostics (Basel, Switzerland) · 2024Review
- Fully-automated, CT-only GTV contouring for palliative head and neck radiotherapy.Scientific reports · 2023Article
- Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography.Bioengineering (Basel, Switzerland) · 2023Review
- X-ray Tomography Applied to Electrochemical Devices and Electrocatalysis.Chemical reviews · 2023Review
- ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images.Biomedical signal processing and control · 2023Article
- Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
- Pelvic U-Net: multi-label semantic segmentation of pelvic organs at risk for radiation therapy anal cancer patients using a deeply supervised shuffle attention convolutional neural network.Radiation oncology (London, England) · 2022Article
- Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors.Scientific reports · 2022Article
- A retrospective study of 3D deep learning approach incorporating coordinate information to improve the segmentation of pre- and post-operative abdominal aortic aneurysm.PeerJ. Computer science · 2022Article
- Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size.Frontiers in physiology · 2022Article
- A 3D deep learning approach to epicardial fat segmentation in non-contrast and post-contrast cardiac CT images.PeerJ. Computer science · 2021Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Computer Tomography (CT) is an imaging procedure that combines many X-ray measurements taken from different angles. The segmentation of areas in the CT images provides a valuable aid to physicians and radiologists in order to better provide a patient diagnose. The CT scans of a body torso usually include different neighboring internal body organs. Deep learning has become the state-of-the-art in medical image segmentation. For such techniques, in order to perform a successful segmentation, it is of great importance that the network learns to focus on the organ of interest and surrounding structures and also that the network can detect target regions of different sizes. In this paper, we propose the extension of a popular deep learning methodology, Convolutional Neural Networks (CNN), by including deep supervision and attention gates. Our experimental evaluation shows that the inclusion of attention and deep supervision results in consistent improvement of the tumor prediction accuracy across the different datasets and training sizes while adding minimal computational overhead.
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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.