Xiaoshuang Shi

Kernel-Based Supervised Discrete Hashing for Image Retrieval

Recently hashing has become an important tool to tackle the problem of large-scale nearest neighbor searching in computer vision. However, learning discrete hashing codes is a very challenging task due to the NP hard optimization problem. In this paper, we propose a novel yet simple kernel-based supervised discrete hashing method via an asymmetric relaxation strategy. Specifically, we present an optimization model with preserving the hashing function and the relaxed linear function simultaneously to reduce the accumulated quantization error between hashing and linear functions. Furthermore, we improve the hashing model by relaxing the hashing function into a general binary code matrix and introducing an additional regularization term. Then we solve these two optimization models via an alternative strategy, which can effectively and stably preserve the similarity of neighbors in a low-dimensional Hamming space. The proposed hashing method can produce informative short binary codes that require less storage volume and lower optimization time cost. Extensive experiments on multiple benchmark databases demonstrate the effectiveness of the proposed hashing method with short binary codes and its superior performance over the state of the arts.

Supervised graph hashing for histopathology image retrieval and classification

HighlightsAn framework based on cell encoding for large‐scale histopathological image analysis is proposed.A supervised graph‐based model via asymmetric relaxation and its scalable version are proposed.A group‐to‐group matching method to retrieve images based on binary codes of cells is proposed. Graphical abstract Figure. No caption available. ABSTRACT In pathology image analysis, morphological characteristics of cells are critical to grade many diseases. With the development of cell detection and segmentation techniques, it is possible to extract cell‐level information for further analysis in pathology images. However, it is challenging to conduct efficient analysis of cell‐level information on a large‐scale image dataset because each image usually contains hundreds or thousands of cells. In this paper, we propose a novel image retrieval based framework for large‐scale pathology image analysis. For each image, we encode each cell into binary codes to generate image representation using a novel graph based hashing model and then conduct image retrieval by applying a group‐to‐group matching method to similarity measurement. In order to improve both computational efficiency and memory requirement, we further introduce matrix factorization into the hashing model for scalable image retrieval. The proposed framework is extensively validated with thousands of lung cancer images, and it achieves 97.98% classification accuracy and 97.50% retrieval precision with all cells of each query image used.

Transfer Shape Modeling Towards High-Throughput Microscopy Image Segmentation

In order to deal with ambiguous image appearances in cell segmentation, high-level shape modeling has been introduced to delineate cell boundaries. However, shape modeling usually requires sufficient annotated training shapes, which are often labor intensive or unavailable. Meanwhile, when applying the model to different datasets, it is necessary to repeat the tedious annotation process to generate enough training data, and this will significantly limit the applicability of the model. In this paper, we propose to transfer shape modeling learned from an existing but different dataset (e.g. lung cancer) to assist cell segmentation in a new target dataset (e.g. skeletal muscle) without expensive manual annotations. Considering the intrinsic geometry structure of cell shapes, we incorporate the shape transfer model into a sparse representation framework with a manifold embedding constraint, and provide an efficient algorithm to solve the optimization problem. The proposed algorithm is tested on multiple microscopy image datasets with different tissue and staining preparations, and the experiments demonstrate its effectiveness.

SemiContour: A Semi-Supervised Learning Approach for Contour Detection

Supervised contour detection methods usually require many labeled training images to obtain satisfactory performance. However, a large set of annotated data might be unavailable or extremely labor intensive. In this paper, we investigate the usage of semi-supervised learning (SSL) to obtain competitive detection accuracy with very limited training data (three labeled images). Specifically, we propose a semi-supervised structured ensemble learning approach for contour detection built on structured random forests (SRF). To allow SRF to be applicable to unlabeled data, we present an effective sparse representation approach to capture inherent structure in image patches by finding a compact and discriminative low-dimensional subspace representation in an unsupervised manner, enabling the incorporation of abundant unlabeled patches with their estimated structured labels to help SRF perform better node splitting. We re-examine the role of sparsity and propose a novel and fast sparse coding algorithm to boost the overall learning efficiency. To the best of our knowledge, this is the first attempt to apply SSL for contour detection. Extensive experiments on the BSDS500 segmentation dataset and the NYU Depth dataset demonstrate the superiority of the proposed method.

Efficient and robust cell detection: A structured regression approach

HighllightsA highly efficient and effective fully residual convolutional neural network is proposed for cell detection.We validate the superiority of structured regression over the conventional pixel wise classification method for cell detection.We prove the robustness and generalization capability of our model using four datasets, each corresponding to a distinct staining method or image modality. Graphical abstract Figure. No Caption available. Abstract Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer‐aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and large variations in cell sizes and shapes. In addition, the ever‐increasing amount of available datasets and the high resolution of whole‐slice scanned images pose a further demand for efficient processing algorithms. In this paper, we present a novel structured regression model based on a proposed fully residual convolutional neural network for efficient cell detection. For each testing image, our model learns to produce a dense proximity map that exhibits higher responses at locations near cell centers. Our method only requires a few training images with weak annotations (just one dot indicating the cell centroids). We have extensively evaluated our method using four different datasets, covering different microscopy staining methods (e.g., H & E or Ki‐67 staining) or image acquisition techniques (e.g., bright‐filed image or phase contrast). Experimental results demonstrate the superiority of our method over existing state of the art methods in terms of both detection accuracy and running time.

Pairwise based deep ranking hashing for histopathology image classification and retrieval

Abstract Hashing has become a popular tool on histopathology image analysis due to the significant gain in both computation and storage. However, most of current hashing techniques learn features and binary codes individually from whole images, or emphasize the inter-class difference but neglect the relevance order within the same classes. To alleviate these issues, in this paper, we propose a novel pairwise based deep ranking hashing framework. We first define a pairwise matrix to preserve intra-class relevance and inter-class difference. Then we propose an objective function that utilizes two identical continuous matrices generated by the hyperbolic tangent (tanh) function to approximate the pairwise matrix. Finally, we incorporate the objective function into a deep learning architecture to learn features and binary codes simultaneously. The proposed framework is validated on 5356 skeletal muscle and 2176 lung cancer images with four types of diseases, and it can achieve 97.49% classification accuracy, 97.49% mean average precision (MAP) with 100 returned images, and 0.51 NDCG score with 50 retrieved neighbors on 2032 query images.

Cell Encoding for Histopathology Image Classification

Although many image analysis algorithms can achieve good performance with sufficient number of labeled images, manually labeling images by pathologists is time consuming and expensive. Meanwhile, with the development of cell detection and segmentation techniques, it is possible to classify pathology images by using cell-level information, which is crucial to grade different diseases; however, it is still very challenging to efficiently conduct cell analysis on large-scale image databases since one image often contains a large number of cells. To address these issues, in this paper, we present a novel cell-based framework that requires only a few labeled images to classify large-scale pathology ones. Specifically, we encode each cell into a set of binary codes to generate image representation using a semi-supervised hashing model, which can take advantage of both labeled and unlabeled cells. Thereafter, we map all the binary codes in one whole image into a single histogram vector and then learn a support vector machine for image classification. The proposed framework is validated on one large-scale lung cancer image dataset with two types of diseases, and it can achieve 87.88% classification accuracy on 800 test images using only 5 labeled images of each disease.

Revisiting graph construction for fast image segmentation

Abstract In this paper, we propose a simple but effective method for fast image segmentation. We re-examine the locality-preserving character of spectral clustering by constructing a graph over image regions with both global and local connections. Our novel approach to build graph connections relies on two key observations: 1) local region pairs that co-occur frequently will have a high probability to reside on a common object; 2) spatially distant regions in a common object often exhibit similar visual saliency, which implies their neighborship in a manifold. We present a novel energy function to efficiently conduct graph partitioning. Based on multiple high quality partitions, we show that the generated eigenvector histogram based representation can automatically drive effective unary potentials for a hierarchical random field model to produce multi-class segmentation. Sufficient experiments, on the BSDS500 benchmark, large-scale PASCAL VOC and COCO datasets, demonstrate the competitive segmentation accuracy and significantly improved efficiency of our proposed method compared with other state of the arts.