AIMC Topic: Observer Variation

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Echocardiography Segmentation With Enforced Temporal Consistency.

IEEE transactions on medical imaging Sep 30, 2022
Convolutional neural networks (CNN) have demonstrated their ability to segment 2D cardiac ultrasound images. However, despite recent successes according to which the intra-observer variability on end-diastole and end-systole images has been reached, ...
Humans Neural Networks, Computer Echocardiography Heart Observer Variation Image Processing, Computer-Assisted
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Deep learning multi-organ segmentation for whole mouse cryo-images including a comparison of 2D and 3D deep networks.

Scientific reports Sep 7, 2022
Cryo-imaging provided 3D whole-mouse microscopic color anatomy and fluorescence images that enables biotechnology applications (e.g., stem cells and metastatic cancer). In this report, we compared three methods of organ segmentation: 2D U-Net with 2D...
Humans Tomography, X-Ray Computed Animals Thorax Deep Learning Mice Abdomen Observer Variation
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Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images.

Scientific reports Sep 6, 2022
Breast cancer tumor grade is strongly associated with patient survival. In current clinical practice, pathologists assign tumor grade after visual analysis of tissue specimens. However, different studies show significant inter-observer variation in b...
Survival Analysis Humans Observer Variation Breast Neoplasms Deep Learning Neoplasm Grading Female Pathologists
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Fully automated mouse echocardiography analysis using deep convolutional neural networks.

American journal of physiology. Heart and circulatory physiology Aug 19, 2022
Echocardiography (echo) is a translationally relevant ultrasound imaging modality widely used to assess cardiac structure and function in preclinical models of heart failure (HF) during research and drug development. Although echo is a very valuable ...
Echocardiography Image Processing, Computer-Assisted Mice Neural Networks, Computer Animals Heart Ventricles Observer Variation Humans
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Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry.

PloS one Jul 29, 2022
Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic ...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Thyroid Gland Observer Variation Ultrasonography Humans Neural Networks, Computer
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Deep learning-based tool affects reproducibility of pes planus radiographic assessment.

Scientific reports Jul 28, 2022
Angle measurement methods for measuring pes planus may lose consistency by errors between observers. If the feature points for angle measurement can be provided in advance with the algorithm developed through the deep learning method, it is thought t...
Weight-Bearing Flatfoot Humans Reproducibility of Results Radiography Deep Learning Observer Variation
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Automatic segmentation of thoracic CT images using three deep learning models.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique Jul 5, 2022
PURPOSE: Deep learning (DL) techniques are widely used in medical imaging and in particular for segmentation. Indeed, manual segmentation of organs at risk (OARs) is time-consuming and suffers from inter- and intra-observer segmentation variability. ...
Deep Learning Image Processing, Computer-Assisted Observer Variation Organs at Risk Humans Tomography, X-Ray Computed
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Radiologists with and without deep learning-based computer-aided diagnosis: comparison of performance and interobserver agreement for characterizing and diagnosing pulmonary nodules/masses.

European radiology Jun 25, 2022
OBJECTIVES: To compare the performance of radiologists in characterizing and diagnosing pulmonary nodules/masses with and without deep learning (DL)-based computer-aided diagnosis (CAD).
Solitary Pulmonary Nodule Humans Diagnosis, Computer-Assisted Radiologists Observer Variation Deep Learning Lung Neoplasms Multiple Pulmonary Nodules Computers Reproducibility of Results Sensitivity and Specificity
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Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm.

Medicina (Kaunas, Lithuania) Jun 9, 2022
Polysomnography is manually scored by sleep experts. However, manual scoring is a time-consuming and labor-intensive task. The goal of this study was to verify the accuracy of automated sleep-stage scoring based on a deep learning algorithm compared...
Adult Male Sleep Observer Variation Deep Learning Female Reproducibility of Results Humans Algorithms Sleep Stages
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Robust Medical Image Classification From Noisy Labeled Data With Global and Local Representation Guided Co-Training.

IEEE transactions on medical imaging Jun 1, 2022
Deep neural networks have achieved remarkable success in a wide variety of natural image and medical image computing tasks. However, these achievements indispensably rely on accurately annotated training data. If encountering some noisy-labeled image...
Neural Networks, Computer Image Processing, Computer-Assisted Humans Observer Variation
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