FRED-Net: Fully residual encoder–decoder network for accurate iris segmentation

Arsalan, Muhammad; Kim, Dong Seop; Lee, Min Beom; Owais, Muhammad; Park, Kang Ryoung

doi:10.1016/j.eswa.2019.01.010

Cited by 73 publications

(59 citation statements)

References 39 publications

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“…Unlike other traditional networks where the final feature map is small (7 × 7) [51], the X-RayNet maintains the final feature map at 21 × 21 for a 350 × 350 CXR image with a total of 17 layers overall. Table 2 lists the key differences of the proposed X-RayNet with deep networks, such as ResNet [52], SegNet [53], IrisDenseNet [54], fully residual encoder-decoder network (FRED-Net) [55], outer residual skip network (OR-Skip-Net) [56], Vess-Net [15], and U-Net [57], in different application domains. Considering the mesh residual structure of X-RayNet, Figure 2 shows the layer connectivity of the candidate encoder and decoder block with a feature empowerment scheme.…”

Section: Chest Anatomy Segmentation Using X-raynetmentioning

confidence: 99%

“…The CXR images are multiclass with a different number of pixels per class; thus, the cross-entropy loss with median frequency balancing was used to quickly train the network. A similar scheme of cross-entropy in combination with frequency balancing was utilized in [53][54][55][56]. Figure 6 shows the training loss and accuracy curves for the proposed X-RayNet.…”

Section: X-raynet Trainingmentioning

confidence: 99%

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Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

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Section: Chest Anatomy Segmentation Using X-raynetmentioning

confidence: 99%

Section: X-raynet Trainingmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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“…The majority of the processes are now automated and can even be used as a second-opinion system in medical diagnosis. AI techniques [3][4][5][6][7][8][9][10] have been developed previously for solving problems in the medical field. Mitotic-cell detection can also be automated using AI techniques; however, it comprises several challenges.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

Mahmood

Arsalan

Owais

et al. 2020

JCM

Self Cite

147

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Breast cancer is the leading cause of mortality in women. Early diagnosis of breast cancer can reduce the mortality rate. In the diagnosis, the mitotic cell count is an important biomarker for predicting the aggressiveness, prognosis, and grade of breast cancer. In general, pathologists manually examine histopathology images under high-resolution microscopes for the detection of mitotic cells. However, because of the minute differences between the mitotic and normal cells, this process is tiresome, time-consuming, and subjective. To overcome these challenges, artificial-intelligence-based (AI-based) techniques have been developed which automatically detect mitotic cells in the histopathology images. Such AI techniques accelerate the diagnosis and can be used as a second-opinion system for a medical doctor. Previously, conventional image-processing techniques were used for the detection of mitotic cells, which have low accuracy and high computational cost. Therefore, a number of deep-learning techniques that demonstrate outstanding performance and low computational cost were recently developed; however, they still require improvement in terms of accuracy and reliability. Therefore, we present a multistage mitotic-cell-detection method based on Faster region convolutional neural network (Faster R-CNN) and deep CNNs. Two open datasets (international conference on pattern recognition (ICPR) 2012 and ICPR 2014 (MITOS-ATYPIA-14)) of breast cancer histopathology were used in our experiments. The experimental results showed that our method achieves the state-of-the-art results of 0.876 precision, 0.841 recall, and 0.858 F1-measure for the ICPR 2012 dataset, and 0.848 precision, 0.583 recall, and 0.691 F1-measure for the ICPR 2014 dataset, which were higher than those obtained using previous methods. Moreover, we tested the generalization capability of our technique by testing on the tumor proliferation assessment challenge 2016 (TUPAC16) dataset and found that our technique also performs well in a cross-dataset experiment which proved the generalization capability of our proposed technique.

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“…Inspired by these pioneering works, in this paper, we propose a new model which can improve the detection robustness without introducing artificial artefacts. Two types of residual learning (He et al 2016b;Arsalan et al 2019) units have been adopted for down-and up-sampling processes in our CNN-based model, thus improving the accuracy without the requirements for large amounts of data. In addition, our model obviates the need for pre-treatments or further polishing, thereby increasing the efficiency and reliability of the process.…”

mentioning

confidence: 99%

Deep residual detection of radio frequency interference for FAST

Zhi-cheng

Xiao

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Radio frequency interference (RFI) detection and excision is one of the key steps in the data processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The FAST telescope, due to its high sensitivity and large data rate, requires more accurate and efficient RFI flagging methods than its counterparts. In the last decades, approaches based upon artificial intelligence (AI), such as codes using Convolutional Neural Network (CNN), have been proposed to identify RFI more reliably and efficiently. However, RFI flagging of FAST data with such methods has often proved to be erroneous, with further manual inspections required. In addition, network construction as well as training dataset preparation for effective RFI flagging has imposed significant additional workloads. Therefore, rapid deployment and adjustment of AI approaches for different observations is impractical to implement with existing algorithms. To overcome such problems, we propose a model named RFI-Net. With the input of raw data without any processing, RFI-Net can detect RFI automatically, producing corresponding masks without any alteration of the original data. Experiments with RFI-Net using simulated astronomical data show that our model has outperformed existing methods in terms of both precision and recall. Besides, compared with other models, our method can obtain the same relative accuracy with less training data, thus saving effort and time required to prepare the training set. Further, the training process of RFI-Net can be accelerated, with overfittings being minimised, compared with other CNN codes. The performance of RFI-Net has also been evaluated with observing data obtained by FAST and Bleien Observatory. Our results demonstrate the ability of RFI-Net to accurately identify RFI with fine-grained, high-precision masks that required no further modification.

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FRED-Net: Fully residual encoder–decoder network for accurate iris segmentation

Cited by 73 publications

References 39 publications

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Artificial Intelligence-Based Mitosis Detection in Breast Cancer Histopathology Images Using Faster R-CNN and Deep CNNs

Deep residual detection of radio frequency interference for FAST

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