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Fuyong Xing

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4 papers

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AAAI Conference 2020 Conference Paper

Loss-Based Attention for Deep Multiple Instance Learning

Xiaoshuang Shi
Fuyong Xing
Yuanpu Xie
Zizhao Zhang
Lei Cui
Lin Yang

Although attention mechanisms have been widely used in deep learning for many tasks, they are rarely utilized to solve multiple instance learning (MIL) problems, where only a general category label is given for multiple instances contained in one bag. Additionally, previous deep MIL methods ﬁrstly utilize the attention mechanism to learn instance weights and then employ a fully connected layer to predict the bag label, so that the bag prediction is largely determined by the effectiveness of learned instance weights. To alleviate this issue, in this paper, we propose a novel loss based attention mechanism, which simultaneously learns instance weights and predictions, and bag predictions for deep multiple instance learning. Speciﬁcally, it calculates instance weights based on the loss function, e. g. softmax+cross-entropy, and shares the parameters with the fully connected layer, which is to predict instance and bag predictions. Additionally, a regularization term consisting of learned weights and crossentropy functions is utilized to boost the recall of instances, and a consistency cost is used to smooth the training process of neural networks for boosting the model generalization performance. Extensive experiments on multiple types of benchmark databases demonstrate that the proposed attention mechanism is a general, effective and efﬁcient framework, which can achieve superior bag and image classiﬁcation performance over other state-of-the-art MIL methods, with obtaining higher instance precision and recall than previous attention mechanisms. Source codes are available on https: //github. com/xsshi2015/Loss-Attention.

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JBHI Journal 2019 Journal Article

Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images

Manish Sapkota
Xiaoshuang Shi
Fuyong Xing
Lin Yang

Compact binary representations of histopathology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality of the query image based on the retrieved similar cases, thereby providing support for medical diagnosis. They also allow for efficient managing of large-scale image databases because of a low storage requirement. However, the effectiveness of binary representations heavily relies on the visual descriptors that represent the semantic information in the histopathological images. Traditional approaches with hand-crafted visual descriptors might fail due to significant variations in image appearance. Recently, deep learning architectures provide promising solutions to address this problem using effective semantic representations. In this paper, we propose a deep convolutional hashing method that can be trained “pointwise” to simultaneously learn both semantic and binary representations of histopathological images. Specifically, we propose a convolutional neural network that introduces a latent binary encoding ('BE) layer for low-dimensional feature embedding to learn binary codes. We design a joint optimization objective function that encourages the network to learn discriminative representations from the label information, and reduce the gap between the real-valued low-dimensional embedded features and desired binary values. The binary encoding for new images can be obtained by forward propagating through the network and quantizing the output of the 'BE layer. Experimental results on a large-scale histopathological image dataset demonstrate the effectiveness of the proposed method.

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JBHI Journal 2018 Journal Article

AIIMDs: An Integrated Framework of Automatic Idiopathic Inflammatory Myopathy Diagnosis for Muscle

Manish Sapkota
Fujun Liu
Yuanpu Xie
Hai Su
Fuyong Xing
Lin Yang

Idiopathic inflammatory myopathy (IIM) is a common skeletal muscle disease that relates to weakness and inflammation of muscle. Early diagnosis and prognosis of different types of IIMs will guide the effective treatment. Interpretation of digitized images of the cross-section muscle biopsy, which is currently done manually, provides the most reliable diagnostic information. With the increasing volume of images, the management and manual interpretation of the digitized muscle images suffer from low efficiency and high interobserver variabilities. In order to address these problems, we propose the first complete framework of automatic IIM diagnosis system for the management and interpretation of digitized skeletal muscle histopathology images. The proposed framework consists of several key components: (1) Automatic cell segmentation, perimysium annotation, and nuclei detection; (2) histogram-based feature extraction and quantification; (3) content-based image retrieval to search and retrieve similar cases in the database for comparative study; and (4) majority voting-based classification to provide decision support for computer-aided clinical diagnosis. Experiments show that the proposed diagnosis system provides efficient and robust interpretation of the digitized muscle image and computer-aided diagnosis of IIM.

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AAAI Conference 2017 Conference Paper

Asymmetric Discrete Graph Hashing

Xiaoshuang Shi
Fuyong Xing
Kaidi Xu
Manish Sapkota
Lin Yang

Recently, many graph based hashing methods have been emerged to tackle large-scale problems. However, there exists two major bottlenecks: (1) directly learning discrete hashing codes is an NP-hard optimization problem; (2) the complexity of both storage and computational time to build a graph with n data points is O(n2 ). To address these two problems, in this paper, we propose a novel yet simple supervised graph based hashing method, asymmetric discrete graph hashing, by preserving the asymmetric discrete constraint and building an asymmetric afﬁnity matrix to learn compact binary codes. Speciﬁcally, we utilize two different instead of identical discrete matrices to better preserve the similarity of the graph with short binary codes. We generate the asymmetric afﬁnity matrix using m (m << n) selected anchors to approximate the similarity among all training data so that computational time and storage requirement can be signiﬁcantly improved. In addition, the proposed method jointly learns discrete binary codes and a low-dimensional projection matrix to further improve the retrieval accuracy. Extensive experiments on three benchmark large-scale databases demonstrate its superior performance over the recent state of the arts with lower training time costs.

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