Book ChapterDOI
Uniqueness-Driven Saliency Analysis for Automated Lesion Detection with Applications to Retinal Diseases
Yitian Zhao,Yalin Zheng,Yifan Zhao,Yonghuai Liu,Zhili Chen,Peng Liu,Jiang Liu +6 more
- pp 109-118
TLDR
A new method to extract uniqueness-driven saliency based on the uniqueness of intensity and spatial distributions within the images is proposed and the experimental results show that the proposed method is superior to the state-of-the-art methods.Abstract:
Saliency is important in medical image analysis in terms of detection and segmentation tasks. We propose a new method to extract uniqueness-driven saliency based on the uniqueness of intensity and spatial distributions within the images. The main novelty of this new saliency feature is that it is powerful in the detection of different types of lesions in different types of images without the need of tuning parameters for different problems. To evaluate its effectiveness, we have applied our method to the detection lesions of retinal images. Four different types of lesions: exudate, hemorrhage, microaneurysms and leakage from 7 independent public retinal image datasets of diabetic retinopathy and malarial retinopathy, were studied and the experimental results show that the proposed method is superior to the state-of-the-art methods.read more
Citations
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Proceedings ArticleDOI
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
TL;DR: This work proposes a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific prediction, based on a novel technique to defend against adversarial evidence by filtering gradients during optimization.
Posted Content
Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
TL;DR: The authors proposed a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific prediction, based on a novel technique to defend against adversarial evidence by filtering gradients during optimization.
Journal ArticleDOI
Structure and Illumination Constrained GAN for Medical Image Enhancement.
Yuhui Ma,Jiang Liu,Yonghuai Liu,Huazhu Fu,Yan Hu,Jun Cheng,Hong Qi,Yufei Wu,Jiong Zhang,Yitian Zhao +9 more
TL;DR: Inspired by CycleGAN based on the global constraints of the adversarial loss and cycle consistency, the proposed CSI-GAN treats low and high quality images as those in two domains and computes local structure and illumination constraints for learning both overall characteristics and local details.
Proceedings ArticleDOI
Sparse-Gan: Sparsity-Constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image
Kang Zhou,Shenghua Gao,Jun Cheng,Zaiwang Gu,Huazhu Fu,Zhi Tu,Jianlong Yang,Yitian Zhao,Jiang Liu +8 more
TL;DR: Wang et al. as discussed by the authors proposed a novel anomaly detection framework termed Sparsity-constrained Generative Adversarial Network (Sparse-GAN) for disease screening where only healthy data are available in the training set.
Posted Content
Sparse-GAN: Sparsity-constrained Generative Adversarial Network for Anomaly Detection in Retinal OCT Image
Kang Zhou,Shenghua Gao,Jun Cheng,Zaiwang Gu,Huazhu Fu,Zhi Tu,Jianlong Yang,Yitian Zhao,Jiang Liu +8 more
TL;DR: A novel anomaly detection framework termed Sparsity-constrained Generative Adversarial Network (Sparse-GAN) for disease screening where only healthy data are available in the training set is proposed and the results show that the proposed method outperforms the state-of-the-art methods.
References
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SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
Proceedings ArticleDOI
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TL;DR: This work proposes a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence, and consistently outperformed existing saliency detection methods.
Proceedings ArticleDOI
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Proceedings ArticleDOI
Global contrast based salient region detection
Proceedings ArticleDOI
the DIARETDB1 diabetic retinopathy database and evaluation protocol
Tomi Kauppi,V. Kalesnykiene,Joni-Kristian Kamarainen,Lasse Lensu,Iiris Sorri,A. Raninen,Raimo Voutilainen,Hannu Uusitalo,Heikki Kälviäinen,Juhani Pietilä +9 more
TL;DR: With the proposed database and protocol, it is possible to compare different algorithms, and correspondingly, analyse their maturity for technology transfer from the research laboratories to the medical practice.
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