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CoProNN: Concept-Based Prototypical Nearest Neighbors for Explaining Vision Models
Mounting evidence in explainability for artificial intelligence (XAI) research suggests that good explanations should be tailored to individual tasks...
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Unified framework model for detecting and organizing medical cancerous images in IoMT systems
One of the challenges that arise when utilizing real-time reaction services, such as constructing deep learning models within the Internet of Medical...
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Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches
Medical imaging plays an essential role in modern healthcare, hel** accurate diagnoses and effective treatment strategies. Still, the quality and...
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Decoupled Consistency for Semi-supervised Medical Image Segmentation
By fully utilizing unlabeled data, the semi-supervised learning (SSL) technique has recently produced promising results in the segmentation of...
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Semantic segmentation in medical images through transfused convolution and transformer networks
Recent decades have witnessed rapid development in the field of medical image segmentation. Deep learning-based fully convolution neural networks...
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Complex Attributed Network Embedding for medical complication prediction
To assure the development of effective treatment plans, it is crucial for understanding the complication relationships among diseases. In practice,...
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Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to...
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Multimodal medical tensor fusion network-based DL framework for abnormality prediction from the radiology CXRs and clinical text reports
Pulmonary disease is a commonly occurring abnormality throughout this world. The pulmonary diseases include Tuberculosis, Pneumothorax, Cardiomegaly,...
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Fuzzy-based cross-image pixel contrastive learning for compact medical image segmentation
Existing medical image segmentation ignore the exploration of inter-class similarity and intra-class variability in pixel semantics, and aim to...
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EPCA—Enhanced Principal Component Analysis for Medical Data Dimensionality Reduction
Innovations in technology from the last one decade have led to the generation of colossal amounts of medical data with comparably low cost. Medical...
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UDCT: lung Cancer detection and classification using U-net and DARTS for medical CT images
Lung cancer is the most fatal disease in recent times. Early detection of the same is very crucial and challenging task. Therefore, proper diagnostic...
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Medical Causality Extraction: A Two-Stage Based Nested Relation Extraction Model
The extraction of medical causality contributes to constructing medical causal knowledge graphs, and enhancing the interpretability of modern medical...
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Early detection of stroke disease using patients previous medical data instil with deep learning
Early detection of any disease and starting its treatment in this early stage are the most important steps in case of any life-threatening disease....
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Privacy-preserving deep learning in medical informatics: applications, challenges, and solutions
Deep Learning (DL) has already shown tremendous potential in designing intelligent clinical support systems in biomedicine. Data privacy plays a...
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Information extraction from electronic medical documents: state of the art and future research directions
In the medical field, a doctor must have a comprehensive knowledge by reading and writing narrative documents, and he is responsible for every...
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An innovative medical image synthesis based on dual GAN deep neural networks for improved segmentation quality
Artificial intelligence networks, precisely deep learning, have emerged as a truly life-impacting potential in healthcare – particularly in the area...
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Application of Federated Learning in Medical Imaging
Artificial intelligence and in particular deep learning have shown great potential in the field of medical imaging. The models can be used to analyze...
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DoubleU-NetPlus: a novel attention and context-guided dual U-Net with multi-scale residual feature fusion network for semantic segmentation of medical images
Accurate segmentation of the region of interest in medical images can provide an essential pathway for devising effective treatment plans for...
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A review of deep learning approaches in clinical and healthcare systems based on medical image analysis
Healthcare is a high-priority sector where people expect the highest levels of care and service, regardless of cost. That makes it distinct from...
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Machine Learning Models for Alzheimer’s Disease Detection Using Medical Images
Human brain is an exclusive, sophisticated, and intricate structure. Neuro-degeneration is the death of neurons which is the ultimate cause of brain...