Part-Aware Mask-Guided Attention for Thorax Disease Classification

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.

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Convolutional Neural Networks with Deep Supervised Feature Learning for Remote Sensing Scene Classification

10.20944/preprints202008.0113.v1 ◽

2020 ◽

Author(s):

Grigorios Tsagkatakis ◽

Panagiotis Tsakalides

Keyword(s):

Remote Sensing ◽

Feature Learning ◽

Ground Truth ◽

Classification Performance ◽

Cross Entropy ◽

Scene Classification ◽

Feature Representations ◽

Benchmark Datasets ◽

Low Dimensional ◽

Fully Connected

State-of-the-art remote sensing scene classification methods employ different Convolutional Neural Network architectures for achieving very high classification performance. A trait shared by the majority of these methods is that the class associated with each example is ascertained by examining the activations of the last fully connected layer, and the networks are trained to minimize the cross-entropy between predictions extracted from this layer and ground-truth annotations. In this work, we extend this paradigm by introducing an additional output branch which maps the inputs to low dimensional representations, effectively extracting additional feature representations of the inputs. The proposed model imposes additional distance constrains on these representations with respect to identified class representatives, in addition to the traditional categorical cross-entropy between predictions and ground-truth. By extending the typical cross-entropy loss function with a distance learning function, our proposed approach achieves significant gains across a wide set of benchmark datasets in terms of classification, while providing additional evidence related to class membership and classification confidence.

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Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs11222718 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2718 ◽

Cited By ~ 5

Author(s):

Zhe Meng ◽

Lingling Li ◽

Licheng Jiao ◽

Zhixi Feng ◽

Xu Tang ◽

...

Keyword(s):

Hyperspectral Image ◽

Multiple Scales ◽

Feature Learning ◽

Classification Performance ◽

Connectivity Pattern ◽

Great Success ◽

Feature Representations ◽

Spatial Features ◽

Discriminative Feature ◽

Correlated Information

The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in HSI classification neglect adequately utilizing the strong complementary yet correlated information from each convolutional layer and only employ the last convolutional layer features for classification. In this paper, we propose a novel fully dense multiscale fusion network (FDMFN) that takes full advantage of the hierarchical features from all the convolutional layers for HSI classification. In the proposed network, shortcut connections are introduced between any two layers in a feed-forward manner, enabling features learned by each layer to be accessed by all subsequent layers. This fully dense connectivity pattern achieves comprehensive feature reuse and enforces discriminative feature learning. In addition, various spectral-spatial features with multiple scales from all convolutional layers are fused to extract more discriminative features for HSI classification. Experimental results on three widely used hyperspectral scenes demonstrate that the proposed FDMFN can achieve better classification performance in comparison with several state-of-the-art approaches.

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Contrasitive Learning for 3D Point Clouds Classification and Shape Completion

10.20944/preprints202109.0112.v1 ◽

2021 ◽

Author(s):

Danish Nazir ◽

Muhammad Zeshan Afzal ◽

Alain Pagani ◽

Marcus Liwicki ◽

Didier Stricker

Keyword(s):

Point Clouds ◽

Classification Performance ◽

Local Feature ◽

Feature Representations ◽

3D Point Clouds ◽

Chamfer Distance ◽

Shape Completion ◽

Number Of Classes ◽

Global And Local

In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

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Contrastive Learning for 3D Point Clouds Classification and Shape Completion

Sensors ◽

10.3390/s21217392 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7392

Author(s):

Danish Nazir ◽

Muhammad Zeshan Afzal ◽

Alain Pagani ◽

Marcus Liwicki ◽

Didier Stricker

Keyword(s):

Point Cloud ◽

Feature Learning ◽

Point Clouds ◽

Classification Performance ◽

Feature Representations ◽

3D Point Clouds ◽

Chamfer Distance ◽

Shape Completion ◽

Number Of Classes

In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as classification, segmentation, detection, and other related applications. Our idea is to add contrastive learning into AutoEncoders to encourage global feature learning of the point cloud classes. It is performed by optimizing triplet loss. Furthermore, local feature representations learning of point cloud is performed by adding the Chamfer distance function. To evaluate the performance of our approach, we utilize the PointNet classifier. We also extend the number of classes for evaluation from 4 to 10 to show the generalization ability of the learned features. Based on our results, embeddings generated from the contrastive AutoEncoder enhances shape completion and classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

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Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images

IEEE Transactions on Image Processing ◽

10.1109/tip.2021.3052711 ◽

2021 ◽

Vol 30 ◽

pp. 2476-2487

Author(s):

Qingji Guan ◽

Yaping Huang ◽

Yawei Luo ◽

Ping Liu ◽

Mingliang Xu ◽

...

Keyword(s):

Feature Learning ◽

Disease Classification ◽

X Ray ◽

Discriminative Feature ◽

Chest X Ray

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Multi-View Modeling Method for Functional MRI Images

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3300 ◽

2021 ◽

Vol 11 (2) ◽

pp. 432-436

Author(s):

Jinlong Zhu ◽

Xiujian Hu ◽

Chao Zhang ◽

Guanglei Sheng

Keyword(s):

Dimensional Space ◽

Feature Learning ◽

Classification Performance ◽

Mapping Method ◽

Classification Model ◽

Fmri Data ◽

Support Vector ◽

Learning Framework ◽

Svm Algorithm ◽

Aided Diagnosis

This paper proposes a new unsupervised fuzzy feature mapping method based on fMRI data and combines it with multi-view support vector machine to construct a classification model for computer-aided diagnosis of autism. Firstly, a multi-output TSK fuzzy system is adopted to map the original feature data to the linear separable high-dimensional space. Then a manifold regularization learning framework is introduced, and a new method of unsupervised fuzzy feature learning is proposed. Finally, a multi-view SVM algorithm is used for classification tasks. The experimental results show that the method in this paper can effectively extract important features from the fMRI data in the resting state and improve the model's interpretability on the premise of ensuring the superior and stable classification performance of the model.

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COVID-19 infection map generation and detection from chest X-ray images

Health Information Science and Systems ◽

10.1007/s13755-021-00146-8 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Aysen Degerli ◽

Mete Ahishali ◽

Mehmet Yamac ◽

Serkan Kiranyaz ◽

Muhammad E. H. Chowdhury ◽

...

Keyword(s):

State Of The Art ◽

Ground Truth ◽

Clinical Use ◽

X Ray ◽

Learning Techniques ◽

Map Generation ◽

Severity Grading ◽

Chest X Ray ◽

Novel Method ◽

Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

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Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs13040742 ◽

2021 ◽

Vol 13 (4) ◽

pp. 742

Author(s):

Jian Peng ◽

Xiaoming Mei ◽

Wenbo Li ◽

Liang Hong ◽

Bingyu Sun ◽

...

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Fundamental Problem ◽

Semantic Representation ◽

Feature Learning ◽

Essential Elements ◽

Complex Scene ◽

Feature Representations ◽

The Right ◽

The Relationship

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

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CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

Applied Sciences ◽

10.3390/app11041380 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1380

Author(s):

Yingbo Zhou ◽

Pengcheng Zhao ◽

Weiqin Tong ◽

Yongxin Zhu

Keyword(s):

Distance Learning ◽

Feature Learning ◽

Image Synthesis ◽

Image Feature ◽

Generative Adversarial Networks ◽

Image Generation ◽

Generative Adversarial Network ◽

Feature Representations ◽

Adversarial Network ◽

Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

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AR-Tri-Training: Tri-Training with Assistant Strategy

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.513-517.1840 ◽

2014 ◽

Vol 513-517 ◽

pp. 1840-1844 ◽

Cited By ~ 1

Author(s):

Long Jie Cui ◽

Hong Li Wang ◽

Rong Yi Cui

Keyword(s):

Learning Strategy ◽

Voice Recognition ◽

Classification Performance ◽

Experimental Results ◽

Training Algorithm ◽

Information Strategy ◽

Testing Rate ◽

Rich Information ◽

Validation Set

The classification performance of the classifier is weakened because the noise samples are introduced for the use of unlabeled samples in Tri-training. In this paper a new Tri-training style algorithm named AR-Tri-training (Tri-training with assistant and rich strategy) is proposed. Firstly, the assistant learning strategy is posed. Then the supporting learner is designed by combining the assistant learning strategy with rich information strategy. The number of mislabeled samples produced in the iterations of three classifiers mutually labeling are reduced by use of the supporting learner, moreover the unlabeled samples and the misclassified samples of validation set can be fully used. The proposed algorithm is applied to voice recognition. The experimental results show that AR-Tri-training algorithm can compensate for the shortcomings of Tri-training algorithm, further improve the testing rate.

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