MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation

Ahmad, Parvez; Jin, Hai; Alroobaea, Roobaea; Qamar, Saqib; Zheng, Ran; Alnajjar, Fady; Aboudi, Fathia

doi:10.1109/access.2021.3122543

Cited by 33 publications

(17 citation statements)

References 34 publications

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“…Myronenko [35] added Resnet-based skip connections and designed a VAE (Variational AutoEncoder) architecture based on the U-Net, which won 1st place on the BraTS 2018 challenge validation dataset with a Dice score value of 0.91000, 0.86680 and 0.82330 for WT, TC and ET, respectively. Ahmad [36] proposed a multiscale hierarchical-based U-Net, which introduced a hierarchical block for merging features to extract multi-scale information.…”

Section: Related Workmentioning

confidence: 99%

ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation

et al. 2022

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Recent years have witnessed a growing interest in the use of U-Net and its improvement. It is one of the classic semantic segmentation networks with an encoder-decoder architecture and is widely used in medical image segmentation. In the series versions of U-Net, U-Net++ has been developed as an improved U-Net by designing an architecture with nested and dense skip connections, and U-Net 3+ has been developed as an improved U-Net++ by taking advantage of full-scale skip connections and deep supervision on full-scale aggregated feature maps. Each network architecture has its own advantages in the use of the encoder and decoder. In this paper, we propose an efficient and lightweight U-Net (ELU-Net) with deep skip connections. The deep skip connections include same-and large-scale skip connections from the encoder to fully extract the features of the encoder. In addition, the proposed ELU-Net with different loss functions is discussed to improve the effect of brain tumor learning including WT (whole tumor), TC (tumor core) and ET (enhance tumor) and a new loss function DFK is designed. The effectiveness of the proposed method is demonstrated for a brain tumor dataset used in the BraTS 2018 Challenge and liver dataset used in the ISBI LiTS 2017 Challenge.

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Section: Related Workmentioning

confidence: 99%

ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation

et al. 2022

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“…(vii) Others: Instead of using only publicly available dataset, some researchers [23,24,[28][29][30][31]33,34,36,37,39,40,47,55,60,72,74,80,81,93] have preferred to not use public dataset to classify DR stages. And few researchers [40,50] have used two dataset, one public and another private, to create their new dataset.…”

Section: Publicly Available Datasetmentioning

confidence: 99%

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Farooq

Arooj

Alroobaea

et al. 2022

Sensors

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Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.

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“…Multiscale feature extraction and aggregation play an important role in improving biomedical image segmentation performance. Ahmad et al [ 18 ] proposed a multiscale hierarchical architecture (MH U-Net), which showed excellent performance in medical image segmentation. MH U-Net was composed of encoder-decoder structure and residual inception.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network with Multiscale Fusion and Attention Mechanism for Skin Diseases Assisted Diagnosis

Wang

Han

et al. 2022

Computational Intelligence and Neuroscience

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Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous feature extraction. To solve this problem, we propose a novel multiscale feature fusion network (MSFA-Net). MSFA-Net can extract feature information at different scales through a multiscale feature fusion structure (MSF) in the network and then calibrate and restore the extracted information to achieve the purpose of melanoma segmentation. Specifically, based on the popular encoder-decoder structure, we designed three functional modules, namely MSF, asymmetric skip connection structure (ASCS), and calibration decoder (Decoder). In addition, a weighted cross-entropy loss and two-stage learning rate optimization strategy are designed to train the network more effectively. Compared qualitatively and quantitatively with the representative neural network methods with encoder-decoder structure, such as U-Net, the proposed method can achieve advanced performance.

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MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation

Cited by 33 publications

References 34 publications

ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation

ELU-Net: An Efficient and Lightweight U-Net for Medical Image Segmentation

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Convolutional Neural Network with Multiscale Fusion and Attention Mechanism for Skin Diseases Assisted Diagnosis

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