Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks

Benjdira, Bilel; Ammar, Adel; Koubâa, Anis; Ouni, Kaïs

doi:10.3390/app10031092

Cited by 34 publications

(26 citation statements)

References 36 publications

(47 reference statements)

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“…This recognition is undertaken through classification among various viruses. We profited from the progress conducted on deep learning [1][2][3][4][5][6][7]. Deep Learning is a sub-field of machine learning dealing with algorithms in tune with the structure and function of the brain-known as artificial neural networks.…”

Section: Covid-19 Diagnosis In Chest X-rays Imagesmentioning

confidence: 99%

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

et al. 2021

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The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper’s innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers; (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest; and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%.

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Section: Covid-19 Diagnosis In Chest X-rays Imagesmentioning

confidence: 99%

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

et al. 2021

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“…Different evaluation criteria have been proposed to assess the quality of the semantic segmentation. Commonly used evaluation metrics for classification are accuracy, precision, recall, F1 score, and intersection-of-union (IoU) [33]. Usually, variations on pixel accuracy and IoU have been used frequently [34].…”

Section: Evaluation Metrics For Performance Measuresmentioning

confidence: 99%

Land Cover Classification Using SegNet with Slope, Aspect, and Multidirectional Shaded Relief Images Derived from Digital Surface Model

Lee¹,

Shin

Lee

2020

Journal of Sensors

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Most object detection, recognition, and classification are performed using optical imagery. Images are unable to fully represent the real-world due to the limited range of the visible light spectrum reflected light from the surfaces of the objects. In this regard, physical and geometrical information from other data sources would compensate for the limitation of the optical imagery and bring a synergistic effect for training deep learning (DL) models. In this paper, we propose to classify terrain features using convolutional neural network (CNN) based SegNet model by utilizing 3D geospatial data including infrared (IR) orthoimages, digital surface model (DSM), and derived information. The slope, aspect, and shaded relief images (SRIs) were derived from the DSM and were used as training data for the DL model. The experiments were carried out using the Vaihingen and Potsdam dataset provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the International Society for Photogrammetry and Remote Sensing (ISPRS). The dataset includes IR orthoimages, DSM, airborne LiDAR data, and label data. The motivation of utilizing 3D data and derived information for training the DL model is that real-world objects are 3D features. The experimental results demonstrate that the proposed approach of utilizing and integrating various informative feature data could improve the performance of the DL for semantic segmentation. In particular, the accuracy of building classification is higher compared with other natural objects because derived information could provide geometric characteristics. Intersection-of-union (IoU) of the buildings for the test data and the new unseen data with combining all derived data were 84.90% and 52.45%, respectively.

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“…Since 2012 [9] deep learning based approaches have shown an attractive efficiency in object segmentation for multiple types of imaging (RGB imaging, aerial imaging, multi-spectral imaging etc.) [10][11][12][13][14]. This was a source of inspiration for the medical imaging community to move their interest towards a great adoption of these approaches for different medical imaging modalities (MRI, CT Scan, 2D Ultrasound, 3D Ultrasound etc.)…”

Section: Introductionmentioning

confidence: 99%

Spinal Cord Segmentation in Ultrasound Medical Imagery

et al. 2020

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In this paper, we study and evaluate the task of semantic segmentation of the spinal cord in ultrasound medical imagery. This task is useful for neurosurgeons to analyze the spinal cord movement during and after the laminectomy surgical operation. Laminectomy is performed on patients that suffer from an abnormal pressure made on the spinal cord. The surgeon operates by cutting the bones of the laminae and the intervening ligaments to relieve this pressure. During the surgery, ultrasound waves can pass through the laminectomy area to give real-time exploitable images of the spinal cord. The surgeon uses them to confirm spinal cord decompression or, occasionally, to assess a tumor adjacent to the spinal cord. The Freely pulsating spinal cord is a sign of adequate decompression. To evaluate the semantic segmentation approaches chosen in this study, we constructed two datasets using images collected from 10 different patients performing the laminectomy surgery. We found that the best solution for this task is Fully Convolutional DenseNets if the spinal cord is already in the train set. If the spinal cord does not exist in the train set, U-Net is the best. We also studied the effect of integrating inside both models some deep learning components like Atrous Spatial Pyramid Pooling (ASPP) and Depthwise Separable Convolution (DSC). We added a post-processing step and detailed the configurations to set for both models.

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Data-Efficient Domain Adaptation for Semantic Segmentation of Aerial Imagery Using Generative Adversarial Networks

Cited by 34 publications

References 36 publications

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

Land Cover Classification Using SegNet with Slope, Aspect, and Multidirectional Shaded Relief Images Derived from Digital Surface Model

Spinal Cord Segmentation in Ultrasound Medical Imagery

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