AIMC Topic:
Image Interpretation, Computer-Assisted

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UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification.

IEEE journal of biomedical and health informatics Dec 4, 2020
Deep learning has achieved remarkable success in the optical coherence tomography (OCT) image classification task with substantial labelled B-scan images available. However, obtaining such fine-grained expert annotations is usually quite difficult an...
Humans Diabetic Retinopathy Tomography, Optical Coherence Retina Supervised Machine Learning Macular Edema Image Interpretation, Computer-Assisted Young Adult Adult Deep Learning Adolescent
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A new deep learning approach integrated with clinical data for the dermoscopic differentiation of early melanomas from atypical nevi.

Journal of dermatological science Dec 2, 2020
BACKGROUND: Timely recognition of malignant melanoma (MM) is challenging for dermatologists worldwide and represents the main determinant for mortality. Dermoscopic examination is influenced by dermatologists' experience and fails to achieve adequate...
Retrospective Studies Female Infant, Newborn Melanoma Skin Neoplasms Humans Nevus Infant Deep Learning Adolescent Child, Preschool Skin Image Interpretation, Computer-Assisted Datasets as Topic Dermoscopy Young Adult Child Reproducibility of Results Adult Diagnosis, Differential Aged Male Aged, 80 and over Middle Aged
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Use of Machine Learning to Improve Echocardiographic Image Interpretation Workflow: A Disruptive Paradigm Change?

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography Dec 1, 2020
Workflow Image Interpretation, Computer-Assisted Humans Machine Learning Echocardiography
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DACH: Domain Adaptation Without Domain Information.

IEEE transactions on neural networks and learning systems Nov 30, 2020
Domain adaptation is becoming increasingly important for learning systems in recent years, especially with the growing diversification of data domains in real-world applications, such as the genetic data from various sequencing platforms and video fe...
Neural Networks, Computer Machine Learning Algorithms Deep Learning Data Interpretation, Statistical Image Interpretation, Computer-Assisted
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Fully‑automated deep‑learning segmentation of pediatric cardiovascular magnetic resonance of patients with complex congenital heart diseases.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance Nov 30, 2020
BACKGROUND: For the growing patient population with congenital heart disease (CHD), improving clinical workflow, accuracy of diagnosis, and efficiency of analyses are considered unmet clinical needs. Cardiovascular magnetic resonance (CMR) imaging of...
Age Factors Deep Learning Magnetic Resonance Imaging Humans Image Interpretation, Computer-Assisted Female Predictive Value of Tests Automation Adolescent Reproducibility of Results Workflow Heart Defects, Congenital Child, Preschool Male Child
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Estimating Local Cellular Density in Glioma Using MR Imaging Data.

AJNR. American journal of neuroradiology Nov 26, 2020
BACKGROUND AND PURPOSE: Increased cellular density is a hallmark of gliomas, both in the bulk of the tumor and in areas of tumor infiltration into surrounding brain. Altered cellular density causes altered imaging findings, but the degree to which ce...
Aged Adult Brain Neoplasms Male Magnetic Resonance Imaging Humans Image Interpretation, Computer-Assisted Middle Aged Machine Learning Glioma Female
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A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals.

Nature communications Nov 26, 2020
Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide trea...
Lymph Nodes Diagnosis, Differential Biopsy Feasibility Studies Humans Coloring Agents Hematoxylin Lymphoma, Large B-Cell, Diffuse Microscopy Hospitals Image Interpretation, Computer-Assisted Deep Learning Eosine Yellowish-(YS) Staining and Labeling
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Training confounder-free deep learning models for medical applications.

Nature communications Nov 26, 2020
The presence of confounding effects (or biases) is one of the most critical challenges in using deep learning to advance discovery in medical imaging studies. Confounders affect the relationship between input data (e.g., brain MRIs) and output variab...
HIV Infections Cohort Studies Child Age Factors Magnetic Resonance Imaging Predictive Value of Tests Radiography Hand Middle Aged Case-Control Studies Deep Learning Adolescent Aged, 80 and over Young Adult Adult Male Age Determination by Skeleton Humans Sex Characteristics Sex Factors Brain Image Interpretation, Computer-Assisted Female Datasets as Topic Aged
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A Single-Shot Region-Adaptive Network for Myotendinous Junction Segmentation in Muscular Ultrasound Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Nov 24, 2020
Tracking the myotendinous junction (MTJ) in consecutive ultrasound images is crucial for understanding the mechanics and pathological conditions of the muscle-tendon unit. However, the lack of reliable and efficient identification of MTJ due to poor ...
Ankle Joint Tendons Deep Learning Ultrasonography Muscle, Skeletal Image Interpretation, Computer-Assisted Female Humans Young Adult Adult Male
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Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Nov 24, 2020
Knee arthroscopy is a complex minimally invasive surgery that can cause unintended injuries to femoral cartilage or postoperative complications, or both. Autonomous robotic systems using real-time volumetric ultrasound (US) imaging guidance hold pote...
Deep Learning Ultrasonography Knee Joint Image Interpretation, Computer-Assisted Humans Algorithms Young Adult Adult Cartilage Arthroscopy Femur
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