Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

Journal: Medical physics

Published Date: Mar 25, 2018

Abstract

PURPOSE: To develop an automatic deep feature classification (DFC) method for distinguishing benign angiomyolipoma without visible fat (AMLwvf) from malignant clear cell renal cell carcinoma (ccRCC) from abdominal contrast-enhanced computer tomography (CE CT) images.

Authors

Hansang Lee

School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea.
Helen Hong

Department of Software Convergence, College of Interdisciplinary Studies for Emerging Industries, Seoul Women's University, 621 Hwarang-ro, Nowon-gu, Seoul, 01797, Korea.
Junmo Kim

School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Korea.
Dae Chul Jung

Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.

Keywords

Angiomyolipoma Carcinoma, Renal Cell Contrast Media Diagnosis, Differential Humans Image Processing, Computer-Assisted Kidney Neoplasms Machine Learning Radiography, Abdominal Sensitivity and Specificity Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (29474742)

Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.

Abstract

Authors

Keywords

External Resources

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