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Anatomic-Constrained Medical Image Synthesis via Physiological Density Samplingdoi:10.1007/978-3-031-72120-5_7Despite substantial progress in utilizing deep learning methods for clinical diagnosis, their efficacy depends on sufficient annotated data, which is often limited available owing to the extensive ...
Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning Marco Parola, ... Olga Di Fede October 2024View PDF Research articleOpen access Main challenges on the curation of large scale datasets for pancreas segmentation using deep le...
Deep learning algorithms have demonstrated remarkable efficacy in various medical image analysis (MedIA) applications. However, recent research highlights a performance disparity in these algorithms when applied to specific subgroups, such as exhibiting
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two origin...
In recent years, deep learning models comprising transformer components have pushed the performance envelope in medical image synthesis tasks. Contrary to convolutional neural networks (CNNs) that use static, local filters, transformers use self-attention mechanisms to permit adaptive, non-local filtering...
Stimulate the process of vascular repair via fibroblast/smooth muscle cell recruitment Contribute to initiation of the coagulation cascadeAlpha granules contain: Fibrinogen vWF Factor V (part of the common pathway of the coagulation cascade) Platelet-derived growth factor (PDGF) Platelet factor-4 ...
Deep Learning Based Rib Centerline Extraction and Labeling[paper] Magnetic Resonance Imaging (MRI) 2016 Medical Image Synthesis with Context-aware Generative Adversarial Networks[paper] Multi-scale and Modality Dropout Learning for Intervertebral Disc Localization and Segmentation[paper] ...
Medical image segmentation is an important tool for current clinical applications. It is the backbone of numerous clinical diagnosis methods, oncological treatments and computer-integrated surgeries. A new class of machine learning algorithm, deep learni
Automated control of medical facilities and devices Multi-modal medical image processing and interpretation 3D image reconstruction Area 4: Automated medicine Automated interpretation of medical records Automated synthesis of patients' data Automated decision making of diagnosis Automated recommendation for ...