The PASCAL Visual Object Classes Challenge

Trends in extreme learning machines

Large, labeled datasets have driven deep learning methods to achieve expert-level performance on a variety of medical imaging tasks. We present CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. We design a labeler to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation. We investigate different approaches to using the uncertainty labels for training convolutional neural networks that output the probability of these observations given the available frontal and lateral radiographs. On a validation set of 200 chest radiographic studies which were manually annotated by 3 board-certified radiologists, we find that different uncertainty approaches are useful for different pathologies. We then evaluate our best model on a test set composed of 500 chest radiographic studies annotated by a consensus of 5 board-certified radiologists, and compare the performance of our model to that of 3 additional radiologists in the detection of 5 selected pathologies. On Cardiomegaly, Edema, and Pleural Effusion, the model ROC and PR curves lie above all 3 radiologist operating points. We release the dataset to the public as a standard benchmark to evaluate performance of chest radiograph interpretation models.

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CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison

https://www.cse.msu.edu/~rossarun/BiometricsTextBook/Papers/Iris/Bowyer_ImageUndIris_CVIU07.pdf

Image understanding for iris biometrics: A survey

Background
Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.

/pdf/deep-learning-for-chest-radiograph-diagnosis-a-retrospective-1tsxdvti5i.pdf

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

This paper considers the task of thorax disease classification on chest X-ray images Existing methods generally use the global image as input for network learning Such a strategy is limited in two aspects 1) A thorax disease usually happens in (small) localized areas which are disease specific Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN) AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch Specifically, we first learn a global CNN branch using global images Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image The local region is used for training a local CNN branch Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch The Comprehensive experiment is conducted on the ChestX-ray14 dataset We first report a strong global baseline producing an average AUC of 0841 with ResNet-50 as backbone After combining the local cues with the global information, AG-CNN improves the average AUC to 0868 While DenseNet-121 is used, the average AUC achieves 0871, which is a new state of the art in the community

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification.

Iris recognition, a relatively new biometric technology, has great advantages, such as variability, stability and security, and is most promising for high security environments. A new iris recognition algorithm is proposed in this paper, which adopts Independent Component Analysis (ICA) to extract iris texture feature and a competitive learning mechanism to recognize iris patterns. Experimental results show that the algorithm is efficient and adaptive to the environment, e.g. it works well even for blurred iris images, variable illumination, and interference of eyelids and eyelashes.

An efficient iris recognition system

Multi-label chest X-ray image classification via category-wise residual attention learning

Just like many other topics in computer vision, image classification has achieved significant progress recently by using deep learning neural networks, especially the Convolutional Neural Networks (CNNs). Most of the existing works focused on classifying very clear natural images, evidenced by the widely used image databases, such as Caltech-256, PASCAL VOCs, and ImageNet. However, in many real applications, the acquired images may contain certain degradations that lead to various kinds of blurring, noise, and distortions. One important and interesting problem is the effect of such degradations to the performance of CNN-based image classification and whether degradation removal helps CNN-based image classification. More specifically, we wonder whether image classification performance drops with each kind of degradation, whether this drop can be avoided by including degraded images into training, and whether existing computer vision algorithms that attempt to remove such degradations can help improve the image classification performance. In this article, we empirically study those problems for nine kinds of degraded images—hazy images, motion-blurred images, fish-eye images, underwater images, low resolution images, salt-and-peppered images, images with white Gaussian noise, Gaussian-blurred images, and out-of-focus images. We expect this article can draw more interests from the community to study the classification of degraded images.

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

In this paper a neural network for solving partial differential equations (PDE) is described The activation functions of the hidden nodes are the radial basis functions (RBF) whose parameters are learnt by a two-stage gradient descent strategy A new growing radial basis functions-node insertion strategy with different radial basis functions is used in order to improve the net performances The learning strategy is able to save computational time and memory space because of the selective growing of nodes whose activation functions consist of different radial basis functions An analysis of the learning capabilities and a comparison of the net performances with other approaches have been performed It is shown that the resulting network improves the approximation results

Yaping Huang

Papers

Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification.

An efficient iris recognition system

Multi-label chest X-ray image classification via category-wise residual attention learning

Effects of Image Degradation and Degradation Removal to CNN-Based Image Classification

Numerical solution of elliptic partial differential equation by growing radial basis function neural networks