AdevianFairuz Pratama

Shenyang Aerospace University

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Zhaoxuan Gong

Shenyang Aerospace University

Dazhe Zhao

Northeastern University

Guodong Zhang

Shenyang Aerospace University

Guodong Zhang

Minzu University of China

Wei Guo

Shenyang Aerospace University

Wei Guo

Shandong University of Science and Technology

Wei Zhou

Shenyang Aerospace University

HasinduMaduranga Suraweera

Shenyang Aerospace University

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Shenyang Aerospace University

Ministry of Education of the People's Republic of China

Northeastern University

Shandong University of Science and Technology

Tsinghua University

Shenyang Institute of Computing Technology (China)

Northeastern University

Suzhou Art & Design Technology Institute

Liaoning Provincial People's Hospital

Yangzhou University

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Automatic Segmentation of Microaneurysm in Retinal Fundus Images

IOP Conference Series Materials Science and Engineering (2020)

Guodong Zhang HasinduMaduranga Suraweera AdevianFairuz Pratama Dazhe Zhao Zhaoxuan Gong

Abstract Diabetes interferes with the body’s ability to use and store sugar (glucose), which it can cause damage throughout the body. Generally, Microaneurysm (MA) is the first clinically observable lesions in the Diabetic Retinopathy (DR). Since interference of biological objects in eye fundus images are similar to MA, locate Microaneurysm lesions precisely is a challenging task for researches. This research employs U-Net Convolutional Neural Network to model saliency of objects in images, global and local context are both taken into account to provide a better initialization for the process. Feature extraction techniques are applied first to assign a local saliency value to each pixel by considering its local context from fundus images such as ORB, SURF, and MSER. The extracted feature vectors are applied for training the network. The sum of the weighted salient object regions produce the final saliency map, then implements U-Net to segment MA lesions.Our experiment has carried out using the publicly available Indian Diabetic Retinopathy Image Dataset (IDRiD), which has used in "Diabetic Retinopathy: Segmentation and Grading Challenge" workshop and our proposed method has given an outstanding accuracy of 98.78 %.

10.1088/1757-899x/768/7/072023

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Effective Representation of Three-Dimension Nodules for False-Positive Reduction in Pulmonary Nodule Detection

Wei Guo AdevianFairuz Pratama Zhaoxuan Gong Wei Zhou Dazhe Zhao

The convolutional neural networks (CNNs) can learn features representation from large amounts of training data, and it has achieved remarkable successes in image processing. However, due to expensive expert annotation and privacy issues, the lack of sufficient training data limits the application of CNNs, especially for 3D CNNs, in medical images.

Representation

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Nodule (geology)

Contextual image classification

10.1145/3364836.3364903

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Citations (1)