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Diagnosis, Differential

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Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists.

AJR. American journal of roentgenology
The objective of our study was to compare the performance of radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation of benign and malignant solid renal masses using contrast-enhanced CT examinations. This ...

A novel automated image analysis system using deep convolutional neural networks can assist to differentiate MDS and AA.

Scientific reports
Detection of dysmorphic cells in peripheral blood (PB) smears is essential in diagnostic screening of hematological diseases. Myelodysplastic syndromes (MDS) are hematopoietic neoplasms characterized by dysplastic and ineffective hematopoiesis, which...

Automated detection and classification of thyroid nodules in ultrasound images using clinical-knowledge-guided convolutional neural networks.

Medical image analysis
Accurate diagnosis of thyroid nodules using ultrasonography is a valuable but tough task even for experienced radiologists, considering both benign and malignant nodules have heterogeneous appearances. Computer-aided diagnosis (CAD) methods could pot...

RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification.

Medical image analysis
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging du...

Radiomics of small renal masses on multiphasic CT: accuracy of machine learning-based classification models for the differentiation of renal cell carcinoma and angiomyolipoma without visible fat.

European radiology
OBJECTIVE: To investigate the discriminative capabilities of different machine learning-based classification models on the differentiation of small (< 4 cm) renal angiomyolipoma without visible fat (AMLwvf) and renal cell carcinoma (RCC).

Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules.

European radiology
PURPOSE: To enhance clinician's decision-making by diagnosing hepatocellular carcinoma (HCC) in cirrhotic patients with indeterminate liver nodules using quantitative imaging features extracted from triphasic CT scans.

Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.

Endoscopy
BACKGROUND: Visual inspection, lesion detection, and differentiation between malignant and benign features are key aspects of an endoscopist's role. The use of machine learning for the recognition and differentiation of images has been increasingly a...

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks.

European journal of cancer (Oxford, England : 1990)
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)
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
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...