AIMC Journal:
Medical image analysis

Showing 321 to 330 of 684 articles

Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Medical image analysis Nov 6, 2021
Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mo...
Male Deep Learning Humans Prostate Prostatectomy Prostatic Neoplasms Magnetic Resonance Imaging
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Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation.

Medical image analysis Nov 3, 2021
Computer-Aided Diagnosis (CAD) for dermatological diseases offers one of the most notable showcases where deep learning technologies display their impressive performance in acquiring and surpassing human experts. In such the CAD process, a critical s...
Disease Progression Software Image Processing, Computer-Assisted Diagnosis, Computer-Assisted Humans Neural Networks, Computer
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Hierarchical graph representations in digital pathology.

Medical image analysis Oct 27, 2021
Cancer diagnosis, prognosis, and therapy response predictions from tissue specimens highly depend on the phenotype and topological distribution of constituting histological entities. Thus, adequate tissue representations for encoding histological ent...
Histological Techniques Benchmarking Humans Neural Networks, Computer Prognosis
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RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data.

Medical image analysis Oct 21, 2021
Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients' features relative to each other. This can be achieved by modeling the problem a...
Neural Networks, Computer Humans
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Curriculum learning for improved femur fracture classification: Scheduling data with prior knowledge and uncertainty.

Medical image analysis Oct 21, 2021
An adequate classification of proximal femur fractures from X-ray images is crucial for the treatment choice and the patients' clinical outcome. We rely on the commonly used AO system, which describes a hierarchical knowledge tree classifying the ima...
Curriculum Uncertainty Femur Neural Networks, Computer Deep Learning Humans
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Deep learning for bone marrow cell detection and classification on whole-slide images.

Medical image analysis Oct 16, 2021
Bone marrow (BM) examination is an essential step in both diagnosing and managing numerous hematologic disorders. BM nucleated differential count (NDC) analysis, as part of BM examination, holds the most fundamental and crucial information. However, ...
Deep Learning Bone Marrow Cells Microscopy
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Self-supervised driven consistency training for annotation efficient histopathology image analysis.

Medical image analysis Oct 13, 2021
Training a neural network with a large labeled dataset is still a dominant paradigm in computational histopathology. However, obtaining such exhaustive manual annotations is often expensive, laborious, and prone to inter and intra-observer variabilit...
Neural Networks, Computer Image Processing, Computer-Assisted Humans Supervised Machine Learning Benchmarking
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Using synthetic data generation to train a cardiac motion tag tracking neural network.

Medical image analysis Sep 10, 2021
A CNN based method for cardiac MRI tag tracking was developed and validated. A synthetic data simulator was created to generate large amounts of training data using natural images, a Bloch equation simulation, a broad range of tissue properties, and ...
Phantoms, Imaging Magnetic Resonance Imaging Neural Networks, Computer Motion Computer Simulation Humans Heart
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A deep-learning approach for direct whole-heart mesh reconstruction.

Medical image analysis Sep 8, 2021
Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. While deep-learning-based approaches have demonstrated promising reconstruction precision, these app...
Humans Image Processing, Computer-Assisted Neural Networks, Computer Imaging, Three-Dimensional Deep Learning Surgical Mesh
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A CNN-based method to reconstruct 3-D spine surfaces from US images in vivo.

Medical image analysis Sep 1, 2021
Three-dimensional (3-D) reconstruction of the spine surface is of strong clinical relevance for the diagnosis and prognosis of spine disorders and intra-operative image guidance. In this paper, we report a new technique to reconstruct lumbar spine su...
Image Processing, Computer-Assisted Animals Rabbits Ultrasonography Lumbar Vertebrae Neural Networks, Computer
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