scholarly journals Corrigendum to “Dual Focal Loss to address class imbalance in semantic segmentation” [Neurocomputing 462 (2021) 69-87]

2022 ◽  
Vol 477 ◽  
pp. 61
Author(s):  
Md Sazzad Hossain ◽  
John M. Betts ◽  
Andrew P. Paplinski
Keyword(s):  
Class Imbalance ◽  
Semantic Segmentation

A Separate 3D Convolutional Neural Network Architecture for 3D Medical Image Semantic Segmentation

2019 ◽  
Vol 9 (8) ◽  
pp. 1705-1716
Author(s):  
Shidu Dong ◽  
Zhi Liu ◽  
Huaqiu Wang ◽  
Yihao Zhang ◽  
Shaoguo Cui
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Network Architecture ◽  
Medical Image ◽  
Negative Impact ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Memory Space

To exploit three-dimensional (3D) context information and improve 3D medical image semantic segmentation, we propose a separate 3D (S3D) convolution neural network (CNN) architecture. First, a two-dimensional (2D) CNN is used to extract the 2D features of each slice in the xy-plane of 3D medical images. Second, one-dimensional (1D) features reassembled from the 2D features in the z-axis are input into a 1D-CNN and are then classified feature-wise. Analysis shows that S3D-CNN has lower time complexity, fewer parameters and less memory space requirements than other 3D-CNNs with a similar structure. As an example, we extend the deep convolutional encoder–decoder architecture (SegNet) to S3D-SegNet for brain tumor image segmentation. We also propose a method based on priority queues and the dice loss function to address the class imbalance for medical image segmentation. The experimental results show the following: (1) S3D-SegNet extended from SegNet can improve brain tumor image segmentation. (2) The proposed imbalance accommodation method can increase the speed of training convergence and reduce the negative impact of the imbalance. (3) S3D-SegNet with the proposed imbalance accommodation method offers performance comparable to that of some state-of-the-art 3D-CNNs and experts in brain tumor image segmentation.

Fully Convolutional Network Method of Semantic Segmentation of Class Imbalance Remote Sensing Images

2019 ◽  
Vol 39 (4) ◽  
pp. 0428004 ◽  
Author(s):  
吴止锾 Wu Zhihuan ◽  
高永明 Gao Yongming ◽  
李磊 Li Lei ◽  
薛俊诗 Xue Junshi
Keyword(s):  
Remote Sensing ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Network Method

Fusion-Based Semantic Segmentation Using Deep Learning Architecture in Case of Very Small Training Dataset

2021 ◽  
Author(s):  
Ganesh R. Padalkar ◽  
Madhuri B. Khambete
Keyword(s):  
Deep Learning ◽  
Real Life ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Training Dataset ◽  
Processing Step ◽  
Effective Training ◽  
Unsupervised Algorithms ◽  
Learning Architectures ◽  
Small Dataset

Semantic segmentation is a pre-processing step in computer vision-based applications. It is the task of assigning a predefined class label to every pixel of an image. Several supervised and unsupervised algorithms are available to classify pixels of an image into predefined object classes. The algorithms, such as random forest and SVM are used to obtain the semantic segmentation. Recently, convolutional neural network (CNN)-based architectures have become popular for the tasks of object detection, object recognition, and segmentation. These deep architectures perform semantic segmentation with far better accuracy than the algorithms that were used earlier. CNN-based deep learning architectures require a large dataset for training. In real life, some of the applications may not have sufficient good quality samples for training of deep learning architectures e.g. medical applications. Such a requirement initiated a need to have a technique of effective training of deep learning architecture in case of a very small dataset. Class imbalance is another challenge in the process of training deep learning architecture. Due to class imbalance, the classifier overclassifies classes with large samples. In this paper, the challenge of training a deep learning architecture with a small dataset and class imbalance is addressed by novel fusion-based semantic segmentation technique which improves segmentation of minor and major classes.

scholarly journals High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3774 ◽  
Author(s):  
Xuran Pan ◽  
Lianru Gao ◽  
Bing Zhang ◽  
Fan Yang ◽  
Wenzhi Liao
Keyword(s):  
High Resolution ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Sensor Data ◽  
Median Frequency ◽  
Feature Maps ◽  
Class Imbalance Problem ◽  
Semantic Labeling

Semantic segmentation of high-resolution aerial images is of great importance in certain fields, but the increasing spatial resolution brings large intra-class variance and small inter-class differences that can lead to classification ambiguities. Based on high-level contextual features, the deep convolutional neural network (DCNN) is an effective method to deal with semantic segmentation of high-resolution aerial imagery. In this work, a novel dense pyramid network (DPN) is proposed for semantic segmentation. The network starts with group convolutions to deal with multi-sensor data in channel wise to extract feature maps of each channel separately; by doing so, more information from each channel can be preserved. This process is followed by the channel shuffle operation to enhance the representation ability of the network. Then, four densely connected convolutional blocks are utilized to both extract and take full advantage of features. The pyramid pooling module combined with two convolutional layers are set to fuse multi-resolution and multi-sensor features through an effective global scenery prior manner, producing the probability graph for each class. Moreover, the median frequency balanced focal loss is proposed to replace the standard cross entropy loss in the training phase to deal with the class imbalance problem. We evaluate the dense pyramid network on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2D semantic labeling dataset, and the results demonstrate that the proposed framework exhibits better performances, compared to the state of the art baseline.

scholarly journals EAR-Net: Efficient Atrous Residual Network for Semantic Segmentation of Street Scenes Based on Deep Learning

2021 ◽  
Vol 11 (19) ◽  
pp. 9119
Author(s):  
Seokyong Shin ◽  
Sanghun Lee ◽  
Hyunho Han
Keyword(s):  
Autonomous Vehicles ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Context Information ◽  
Residual Network ◽  
Residual Learning ◽  
Conventional Methods ◽  
Street Scene ◽  
Similar Computation ◽  
Ablation Study

Segmentation of street scenes is a key technology in the field of autonomous vehicles. However, conventional segmentation methods achieve low accuracy because of the complexity of street landscapes. Therefore, we propose an efficient atrous residual network (EAR-Net) to improve accuracy while maintaining computation costs. First, we performed feature extraction and restoration, utilizing depthwise separable convolution (DSConv) and interpolation. Compared with conventional methods, DSConv and interpolation significantly reduce computation costs while minimizing performance degradation. Second, we utilized residual learning and atrous spatial pyramid pooling (ASPP) to achieve high accuracy. Residual learning increases the ability to extract context information by preventing the problem of feature and gradient losses. In addition, ASPP extracts additional context information while maintaining the resolution of the feature map. Finally, to alleviate the class imbalance between the image background and objects and to improve learning efficiency, we utilized focal loss. We evaluated EAR-Net on the Cityscapes dataset, which is commonly used for street scene segmentation studies. Experimental results showed that the EAR-Net had better segmentation results and similar computation costs as the conventional methods. We also conducted an ablation study to analyze the contributions of the ASPP and DSConv in the EAR-Net.

scholarly journals Generative multi-adversarial network for striking the right balance in abdominal image segmentation

2020 ◽  
Vol 15 (11) ◽  
pp. 1847-1858
Author(s):  
Mina Rezaei ◽  
Janne J. Näppi ◽  
Christoph Lippert ◽  
Christoph Meinel ◽  
Hiroyuki Yoshida
Keyword(s):  
Medical Images ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Training Data ◽  
Class Imbalance Problem ◽  
Abdominal Image ◽  
Single Generator ◽  
Adversarial Network ◽  
Imbalance Problem ◽  
Segmentation Image

Abstract Purpose The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images. Method The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017. Result In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively. Conclusion The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts.

scholarly journals Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3183 ◽  
Author(s):  
Zia Khan ◽  
Norashikin Yahya ◽  
Khaled Alsaih ◽  
Syed Saad Azhar Ali ◽  
Fabrice Meriaudeau
Keyword(s):  
Prostate Cancer ◽  
Data Augmentation ◽  
Prostate Gland ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Lesion Detection ◽  
Dice Similarity Coefficient ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Mri Image

In this paper, we present an evaluation of four encoder–decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder–decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to 92.8 % . This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.

scholarly journals On Improving the Training of Models for the Semantic Segmentation of Benthic Communities from Orthographic Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3106
Author(s):  
Gaia Pavoni ◽  
Massimiliano Corsini ◽  
Marco Callieri ◽  
Giuseppe Fiameni ◽  
Clinton Edwards ◽  
...  
Keyword(s):  
Coral Reef ◽  
Benthic Communities ◽  
Model Performance ◽  
Class Imbalance ◽  
Predictive Ability ◽  
Sampling Strategy ◽  
Semantic Segmentation ◽  
Loss Functions ◽  
Class Imbalance Problem ◽  
Segmentation Task

The semantic segmentation of underwater imagery is an important step in the ecological analysis of coral habitats. To date, scientists produce fine-scale area annotations manually, an exceptionally time-consuming task that could be efficiently automatized by modern CNNs. This paper extends our previous work presented at the 3DUW’19 conference, outlining the workflow for the automated annotation of imagery from the first step of dataset preparation, to the last step of prediction reassembly. In particular, we propose an ecologically inspired strategy for an efficient dataset partition, an over-sampling methodology targeted on ortho-imagery, and a score fusion strategy. We also investigate the use of different loss functions in the optimization of a Deeplab V3+ model, to mitigate the class-imbalance problem and improve prediction accuracy on coral instance boundaries. The experimental results demonstrate the effectiveness of the ecologically inspired split in improving model performance, and quantify the advantages and limitations of the proposed over-sampling strategy. The extensive comparison of the loss functions gives numerous insights on the segmentation task; the Focal Tversky, typically used in the context of medical imaging (but not in remote sensing), results in the most convenient choice. By improving the accuracy of automated ortho image processing, the results presented here promise to meet the fundamental challenge of increasing the spatial and temporal scale of coral reef research, allowing researchers greater predictive ability to better manage coral reef resilience in the context of a changing environment.

Dual Focal Loss to address class imbalance in semantic segmentation

2021 ◽  
Author(s):  
Md Sazzad Hossain ◽  
John M. Betts ◽  
Andrew P. Paplinski
Keyword(s):  
Class Imbalance ◽  
Semantic Segmentation

scholarly journals COUNTING ICE-WEDGE POLYGONS FROM SPACE: USE OF COMMERCIAL SATELLITE IMAGERY TO MONITOR CHANGING ARCTIC POLYGONAL TUNDRA

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 67-72
Author(s):  
A. Hasan ◽  
M. R. Udawalpola ◽  
C. Witharana ◽  
A. K. Liljedahl
Keyword(s):  
Satellite Imagery ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Training Data ◽  
The Arctic ◽  
Landscape Complexity ◽  
Training Samples ◽  
Decadal Scale ◽  
Satellite Sensors ◽  
Ice Wedge

Abstract. The microtopography associated with ice wedge polygons (IWPs) governs the Arctic ecosystem from local to regional scales due to the impacts on the flow and storage of water and therefore, vegetation and carbon. Increasing subsurface temperatures in Arctic permafrost landscapes cause differential ground settlements followed by a series of adverse microtopographic transitions at sub decadal scale. The entire Arctic has been imaged at 0.5 m or finer resolution by commercial satellite sensors. Dramatic microtopographic transformation of low-centered into high-centered IWPs can be identified using sub-meter resolution commercial satellite imagery. In this exploratory study, we have employed a Deep Learning (DL)-based object detection and semantic segmentation method named the Mask R-CNN to automatically map IWPs from commercial satellite imagery. Different tundra vegetation types have distinct spectral, spatial, textural characteristics, which in turn decide the semantics of overlying IWPs. Landscape complexity translates to the image complexity, affecting DL model performances. Scarcity of labelled training images, inadequate training samples for some types of tundra and class imbalance stand as other key challenges in this study. We implemented image augmentation methods to introduce variety in the training data and trained models separately for tundra types. Augmentation methods show promising results but the models with separate tundra types seem to suffer from the lack of annotated data.

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