Convolutional neural networks (CNNs) are widely recognized as the foundation for machine vision systems. The conventional rule of teaching CNNs to understand images requires training images with human annotated labels, without any additional instructions. In this article, we look into a new scope and explore the guidance from text for neural network training. We present two versions of attention mechanisms to facilitate interactions between visual and semantic information and encourage CNNs to effectively distill visual features by leveraging semantic features. In contrast to dedicated text-image joint embedding methods, our method realizes asynchronous training and inference behavior: a trained model can classify images, irrespective of the text availability. This characteristic substantially improves the model scalability to multiple (multimodal) vision tasks. We also apply the proposed method onto medical imaging, which learns from richer clinical knowledge and achieves attention-based interpretable decision-making. With comprehensive validation on two natural and two medical datasets, we demonstrate that our method can effectively make use of semantic knowledge to improve CNN performance. Our method performs substantial improvement on medical image datasets. Meanwhile, it achieves promising performance for multi-label image classification and caption-image retrieval as well as excellent performance for phrase-based and multi-object localization on public benchmarks.

Maintaining the pairwise relationship among originally high-dimensional data into a low-dimensional binary space is a popular strategy to learn binary codes. One simple and intuitive method is to utilize two identical code matrices produced by hash functions to approximate a pairwise real label matrix. However, the resulting quartic problem in term of hash functions is difficult to directly solve due to the non-convex and non-smooth nature of the objective. In this paper, unlike previous optimization methods using various relaxation strategies, we aim to directly solve the original quartic problem using a novel alternative optimization mechanism to linearize the quartic problem by introducing a linear regression model. Additionally, we find that gradually learning each batch of binary codes in a sequential mode, i.e. batch by batch, is greatly beneficial to the convergence of binary code learning. Based on this significant discovery and the proposed strategy, we introduce a scalable symmetric discrete hashing algorithm that gradually and smoothly updates each batch of binary codes. To further improve the smoothness, we also propose a greedy symmetric discrete hashing algorithm to update each bit of batch binary codes. Moreover, we extend the proposed optimization mechanism to solve the non-convex optimization problems for binary code learning in many other pairwise based hashing algorithms. Extensive experiments on benchmark single-label and multi-label databases demonstrate the superior performance of the proposed mechanism over recent state-of-the-art methods on two kinds of retrieval tasks: similarity and ranking order. The source codes are available on https://github.com/xsshi2015/Scalable-Pairwise-based-Discrete-Hashing.

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 cross-entropy 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.

The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements. This partial view naturally induces reconstruction uncertainty that can only be reduced by acquiring additional measurements. In this paper, we present a novel method for MRI reconstruction that, at inference time, dynamically selects the measurements to take and iteratively refines the prediction in order to best reduce the reconstruction error and, thus, its uncertainty. We validate our method on a large scale knee MRI dataset, as well as on ImageNet. Results show that (1) our system successfully outperforms active acquisition baselines; (2) our uncertainty estimates correlate with error maps; and (3) our ResNet-based architecture surpasses standard pixel-to-pixel models in the task of MRI reconstruction. The proposed method not only shows high-quality reconstructions but also paves the road towards more applicable solutions for accelerating MRI.

BACKGROUND: Fast and accurate automatic segmentation of skeletal muscle cell image is crucial for the diagnosis of muscle related diseases, which extremely reduces the labor-intensive manual annotation. Recently, several methods have been presented for automatic muscle cell segmentation. However, most methods exhibit high model complexity and time cost, and they are not adaptive to large-scale images such as whole-slide scanned specimens. METHODS: In this paper, we propose a novel distributed computing approach, which adopts both data and model parallel, for fast muscle cell segmentation. With a master-worker parallelism manner, the image data in the master is distributed onto multiple workers based on the Spark cloud computing platform. On each worker node, we first detect cell contours using a structured random forest (SRF) contour detector with fast parallel prediction and generate region candidates using a superpixel technique. Next, we propose a novel hierarchical tree based region selection algorithm for cell segmentation based on the conditional random field (CRF) algorithm. We divide the region selection algorithm into multiple sub-problems, which can be further parallelized using multi-core programming. RESULTS: We test the performance of the proposed method on a large-scale haematoxylin and eosin (H &E) stained skeletal muscle image dataset. Compared with the standalone implementation, the proposed method achieves more than 10 times speed improvement on very large-scale muscle images containing hundreds to thousands of cells. Meanwhile, our proposed method produces high-quality segmentation results compared with several state-of-the-art methods. CONCLUSIONS: This paper presents a parallel muscle image segmentation method with both data and model parallelism on multiple machines. The parallel strategy exhibits high compatibility to our muscle segmentation framework. The proposed method achieves high-throughput effective cell segmentation on large-scale muscle images.

BACKGROUND: Nucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. RESULTS: We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. CONCLUSIONS: We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

Following publication of the original article [1], we have been notified of a few errors in the html version.

Synthesized medical images have several important applications, e.g., as an intermedium in cross-modality image registration and as supplementary training samples to boost the generalization capability of a classifier. Especially, synthesized computed tomography (CT) data can provide X-ray attenuation map for radiation therapy planning. In this work, we propose a generic cross-modality synthesis approach with the following targets: 1) synthesizing realistic looking 3D images using unpaired training data, 2) ensuring consistent anatomical structures, which could be changed by geometric distortion in cross-modality synthesis and 3) improving volume segmentation by using synthetic data for modalities with limited training samples. We show that these goals can be achieved with an end-to-end 3D convolutional neural network (CNN) composed of mutually-beneficial generators and segmentors for image synthesis and segmentation tasks. The generators are trained with an adversarial loss, a cycle-consistency loss, and also a shape-consistency loss, which is supervised by segmentors, to reduce the geometric distortion. From the segmentation view, the segmentors are boosted by synthetic data from generators in an online manner. Generators and segmentors prompt each other alternatively in an end-to-end training fashion. With extensive experiments on a dataset including a total of 4,496 CT and magnetic resonance imaging (MRI) cardiovascular volumes, we show both tasks are beneficial to each other and coupling these two tasks results in better performance than solving them exclusively.

The inability to interpret the model prediction in semantically and visually meaningful ways is a well-known shortcoming of most existing computer-aided diagnosis methods. In this paper, we propose MDNet to establish a direct multimodal mapping between medical images and diagnostic reports that can read images, generate diagnostic reports, retrieve images by symptom descriptions, and visualize attention, to provide justifications of the network diagnosis process. MDNet includes an image model and a language model. The image model is proposed to enhance multi-scale feature ensembles and utilization efficiency. The language model, integrated with our improved attention mechanism, aims to read and explore discriminative image feature descriptions from reports to learn a direct mapping from sentence words to image pixels. The overall network is trained end-to-end by using our developed optimization strategy. Based on a pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we conduct sufficient experiments to demonstrate that MDNet outperforms comparative baselines. The proposed image model obtains state-of-the-art performance on two CIFAR datasets as well.

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.

In this paper, we propose a novel discriminative filter based regression learning (DFRL) method, which can effectively remove irrelevant information while preserving useful information for facial expression recognition. DFRL integrates the filter technique and the linear analysis techniques (i.e., Linear Discriminant Analysis-LDA and Linear Ridge Regression-LRR) to obtain an effective image representation. Two steps are involved in DFRL: 1) The discriminative filters corresponding to different facial expressions are separately trained by optimizing the cost function of the two-class LDA, 2) LRR is used to extract valuable expressional information with high discriminability from the combined filtered images. Experimental results on several challenging datasets demonstrate the superior effectiveness and generalization ability of the proposed DFRL compared with other competing methods.