AIMC Topic: Diagnosis, Differential
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Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.
European journal of cancer (Oxford, England : 1990)
Aug 14, 2019
BACKGROUND: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer s...
Machine learning defined diagnostic criteria for differentiating pituitary metastasis from autoimmune hypophysitis in patients undergoing immune checkpoint blockade therapy.
European journal of cancer (Oxford, England : 1990)
Aug 12, 2019
PURPOSE: New-onset pituitary gland lesions are observed in up to 18% of cancer patients undergoing treatment with immune checkpoint blockers (ICB). We aimed to develop and validate an imaging-based decision-making algorithm for use by the clinician t...
An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Aug 11, 2019
Cancer has been one of the most threatening diseases to human health. There have been many efforts devoted to the advancement of radiology and transformative tools (e.g. non-invasive computed tomographic or CT imaging) to detect cancer in early stage...
Accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema.
BMC ophthalmology
Aug 9, 2019
BACKGROUND: This study is to evaluate the accuracy of machine learning for differentiation between optic neuropathies, pseudopapilledema (PPE) and normals.
Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: A preliminary study.
Diagnostic and interventional imaging
Jul 30, 2019
PURPOSE: The purpose of this study was to evaluate the ability of deep learning to differentiate pancreatic diseases on contrast-enhanced magnetic resonance (MR) images with the aid of generative adversarial network (GAN).
Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor Imaging.
International journal of radiation oncology, biology, physics
Jul 22, 2019
PURPOSE: A noninvasive diagnostic method to predict the degree of malignancy accurately would be of great help in glioma management. This study aimed to create a highly accurate machine learning model to perform glioma grading.
Adult
Brain Neoplasms
Male
Middle Aged
Adolescent
Humans
Diffusion Tensor Imaging
Female
Machine Learning
Anisotropy
Glioma
Aged
Retrospective Studies
Diffusion Magnetic Resonance Imaging
Diagnosis, Differential
Astrocytoma
Aged, 80 and over
Glioblastoma
Young Adult
Oligodendroglioma
Neoplasm Grading
Datasets as Topic
Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images.
European journal of cancer (Oxford, England : 1990)
Jul 18, 2019
BACKGROUND: The diagnosis of most cancers is made by a board-certified pathologist based on a tissue biopsy under the microscope. Recent research reveals a high discordance between individual pathologists. For melanoma, the literature reports on 25-2...
Computer algorithms show potential for improving dermatologists' accuracy to diagnose cutaneous melanoma: Results of the International Skin Imaging Collaboration 2017.
Journal of the American Academy of Dermatology
Jul 12, 2019
BACKGROUND: Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.
The "MS-ROM/IFAST" Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy.
Clinical EEG and neuroscience
Jul 11, 2019
. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with a...
Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study.
The British journal of radiology
Jul 10, 2019
OBJECTIVE: Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in pa...
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