With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. Since manual annotation suffers limited reproducibility, arduous efforts, and excessive time, automatic segmentation is desired to process increasingly larger scale histopathological data. Recently, deep neural networks (DNNs), particularly fully convolutional networks (FCNs), have been widely applied to biomedical image segmentation, attaining much improved performance. At the same time, quantization of DNNs has become an active research topic, which aims to represent weights with less memory (precision) to considerably reduce memory and computation requirements of DNNs while maintaining acceptable accuracy. In this paper, we apply quantization techniques to FCNs for accurate biomedical image segmentation. Unlike existing literatures on quantization which primarily targets memory and computation complexity reduction, we apply quantization as a method to reduce overfitting in FCNs for better accuracy. Specifically, we focus on a state-of-the-art segmentation framework, suggestive annotation [26], which judiciously extracts representative annotation samples from the original training dataset, obtaining an effective small-sized balanced training dataset. We develop two new quantization processes for this framework: (1) suggestive annotation with quantization for highly representative training samples, and (2) network training with quantization for high accuracy. Extensive experiments on the MICCAI Gland dataset show that both quantization processes can improve the segmentation performance, and our proposed method exceeds the current state-of-the-art performance by up to 1 . In addition, our method has a reduction of up to 6.4x on memory usage.
In this section, we briefly review suggestive annotation @cite_0 , on which our proposed method is based. Several representative quantization methods are discussed in detail, which will be adopted in our experiments. The readers are also referred to @cite_22 @cite_10 @cite_7 for other quantization methods.
@cite_0 @cite_10 @cite_22 @cite_7
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In this paper, we present a new temporal quantization-based method using repeated weighted boosting search (RWBS) to navigate the video content non-uniformly. In particular, we formulate the rapid video navigation problem as a generic sampling problem. We present a video temporal density function (VTDF) based on the inter-frame mutual information to describe the time density of video activities. A new VTDF based temporal quantization method using RWBS is then applied to find the best quanta and partition in time domain. The video frames that are the nearest neighbors to the quanta in the quantization codebook are sampled to navigate the video. A video player is implemented based on the proposed method to navigate all sampled frames in its intelligent fast-forward mode. The implementation of video player demonstrates the feasibility of proposed method in practice. Experimental results show that the proposed method is effective to capture the important semantic information of video during rapid navigation. We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two level product and vector quantization tree that reduces the number of vector comparisons required during tree traversal. Our approach also includes a novel highly parallelizable re-ranking method for candidate vectors by efficiently reusing already computed intermediate values. Due to its small memory footprint during traversal the method lends itself to an efficient, parallel GPU implementation. This Product Quantization Tree (PQT) approach significantly outperforms recent state of the art methods for high dimensional nearest neighbor queries on standard reference datasets. Ours is the first work that demonstrates GPU performance superior to CPU performance on high dimensional, large scale ANN problems in time-critical real-world applications, like loop-closing in videos. With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. Since manual annotation suffers limited reproducibility, arduous efforts, and excessive time, automatic segmentation is desired to process increasingly larger scale histopathological data. Recently, deep neural networks (DNNs), particularly fully convolutional networks (FCNs), have been widely applied to biomedical image segmentation, attaining much improved performance. At the same time, quantization of DNNs has become an active research topic, which aims to represent weights with less memory (precision) to considerably reduce memory and computation requirements of DNNs while maintaining acceptable accuracy. In this paper, we apply quantization techniques to FCNs for accurate biomedical image segmentation. Unlike existing literatures on quantization which primarily targets memory and computation complexity reduction, we apply quantization as a method to reduce overfitting in FCNs for better accuracy. Specifically, we focus on a state-of-the-art segmentation framework, suggestive annotation [26], which judiciously extracts representative annotation samples from the original training dataset, obtaining an effective small-sized balanced training dataset. We develop two new quantization processes for this framework: (1) suggestive annotation with quantization for highly representative training samples, and (2) network training with quantization for high accuracy. Extensive experiments on the MICCAI Gland dataset show that both quantization processes can improve the segmentation performance, and our proposed method exceeds the current state-of-the-art performance by up to 1 . In addition, our method has a reduction of up to 6.4x on memory usage. This paper proposes a no-reference quality assessment metric for images subject to quantization noise in block-based DCT (discrete cosine transform) domain, as those resulting from JPEG or MPEG encoding. The proposed method is based on natural scene statistics of the DCT coefficients, whose distribution is usually modeled by a Laplace probability density function, with parameter @l. A new method for @l estimation from quantized coefficient data is presented; it combines maximum-likelihood with linear prediction estimates, exploring the correlation between @l values at adjacent DCT frequencies. The resulting coefficient distributions are then used for estimating the local error due to lossy encoding. Local error estimates are also perceptually weighted, using a well-known perceptual model by Watson. When confronted with subjective quality evaluation data, results show that the quality scores that result from the proposed algorithm are well correlated with the human perception of quality. Since no knowledge about the original (reference) images is required, the proposed method resembles a no-reference quality metric for image evaluation.