AIMC Topic: Observer Variation

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Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis.

Medical image analysis
Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Rec...

Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading.

Virchows Archiv : an international journal of pathology
The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficu...

Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma.

Laboratory investigation; a journal of technical methods and pathology
A pathological evaluation is one of the most important methods for the diagnosis of malignant lymphoma. A standardized diagnosis is occasionally difficult to achieve even by experienced hematopathologists. Therefore, established procedures including ...

Fully Automated Segmentation of Globes for Volume Quantification in CT Images of Orbits using Deep Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Fast and accurate quantification of globe volumes in the event of an ocular trauma can provide clinicians with valuable diagnostic information. In this work, an automated workflow using a deep learning-based convolutional neur...

A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).

Expert review of molecular diagnostics
BACKGROUND: A key objective in glaucoma is to identify those at risk of rapid progression and blindness. Recently, a novel first-in-man method for visualising apoptotic retinal cells called DARC (Detection-of-Apoptosing-Retinal-Cells) was reported. T...

Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT.

European journal of nuclear medicine and molecular imaging
PURPOSE: In selective internal radiation therapy (SIRT), an accurate total liver segmentation is required for activity prescription and absorbed dose calculation. Our goal was to investigate the feasibility of using automatic liver segmentation based...

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans.

The Journal of laryngology and otology
OBJECTIVE: Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algori...

Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study.

BMC musculoskeletal disorders
BACKGROUND: MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiolog...