Chromosome Classification and Straightening Based on an Interleaved and Multi-Task Network

Zhang, Jiping; Hu, Wenjing; Li, Shuyuan; Wen, Yaofeng; Bao, Yong; Huang, He‐Feng; Xu, Chenming; Qian, Dahong

doi:10.1109/jbhi.2021.3062234

Cited by 24 publications

(7 citation statements)

References 33 publications

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“…We also note that our method is generally superior to existing advanced methods but slightly inferior to them in classifying some individual classes. In recent years, both Varifocal‐net [ 19 ] and Muna AI kharraz et al [ 26 ] have used the ensemble learning method, which shows lower degradation in type classification, suggesting that ensemble learning may be a method to improve universality. Moreover, Zhang et al [ 25 ] used multimodality in their method to fully learn the multiscale features of chromosomes.…”

Section: Discussionmentioning

confidence: 99%

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SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Liu

Lin

et al. 2022

IET Systems Biology

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Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super‐resolution network, Self‐Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS‐net. The method first inputs the low‐resolution chromosome images into the super‐resolution network to generate high‐resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state‐of‐the‐art methods.

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Section: Discussionmentioning

confidence: 99%

“…Zhang et al. [ 26 ] proposed a chromosome classification and straightening method based on an interleaved and multi‐task network. First, the method learnt multi‐scale features via an interleaved network.…”

Section: Related Workmentioning

confidence: 99%

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Liu

Lin

et al. 2022

IET Systems Biology

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“…Edwards syndrome, Turner syndrome, and Down syndrome) [3], [4] and other genetic disorders as an important clinical procedure [5]. It is obtained from stained intermediate chromosomes by using staining techniques [6]. The karyotype analysis method analyzes microscopic images of chromosomes in intermediate divisions by banding techniques and then diagnoses diseases based on changes in the structure and number of chromosomes [7].…”

Section: Introductionmentioning

confidence: 99%

Feature Fusion Classifier With Dynamic Weights for Abnormality Detection of Amniotic Fluid Cell Chromosome

Shi

Liu

et al. 2023

IEEE Access

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Chromosomal karyotype is important to determine whether a newborn has a genetic disorder. There are two main categories of chromosomal abnormalities, structural abnormalities in which the chromosome structure is altered, and chromosome number abnormalities. Manual karyotyping is complex and takes a lot of time because it requires a high degree of domain expertise. Based on this investigation proposes a new method of chromosome defect detection based on deep learning with 20,299 chromosome images from Dongguan Kanghua Hospital as data and integrates the diversity of chromosome features, and trains to propose a classifier model based on feature fusion for chromosome abnormality detection. We put forward a feature fusion classifier with dynamic weights (FFCDW) for chromosomal abnormality detection, after data augmentation with three deep learning networks, ResNet, SENet, and VGG19, the three trained models are combined using a dynamic weighting approach. Experiments prove the FFCDW method outperforms these mainstream models of ResNet, SENet, and VGG19. The proposed method based on FFCDW achieves precision of 0.8902 and F1-score of 0.8805 with a small standard deviation (0.00903 and 0.00892, respectively). In addition, the algorithm can automatically assign weights based on the results of a single model, and the strategy with dynamic weights outperforms the strategy with fixed weights in the proposed feature fusion classifier.

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“…(i) Printed images of bent chromosome are physically cut and rearranged for straightening [4], [5]. (ii) Geometric algorithms are extensively designed based on chromosome micrographs for automatic straightening, which consist of two main categories: extraction of the medial axis [6]- [9] and finding bending points [10]- [12]. However, these geometric methods may extract inaccurate medial axes when chromosomes have large widths.…”

Section: Introductionmentioning

confidence: 99%

A Robust Framework of Chromosome Straightening with ViT-Patch GAN

Song¹,

Wang²,

Cheng³

et al. 2022

Preprint

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Chromosomes exhibit non-rigid and non-articulated nature with varying degrees of curvature. Chromosome straightening is an essential step for subsequent karyotype construction, pathological diagnosis and cytogenetic map development. However, robust chromosome straightening remains challenging, due to the unavailability of training images, distorted chromosome details and shapes after straightening, as well as poor generalization capability. We propose a novel architecture, ViT-Patch GAN, consisting of a motion transformation generator and a Vision Transformer-based patch (ViT-Patch) discriminator. The generator learns the motion representation of chromosomes for straightening. With the help of the ViT-Patch discriminator, the straightened chromosomes retain more shape and banding pattern details. The proposed framework is trained on a small dataset and is able to straighten chromosome images with stateof-the-art performance for two large datasets.

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Chromosome Classification and Straightening Based on an Interleaved and Multi-Task Network

Cited by 24 publications

References 33 publications

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

SRAS‐net: Low‐resolution chromosome image classification based on deep learning

Feature Fusion Classifier With Dynamic Weights for Abnormality Detection of Amniotic Fluid Cell Chromosome

A Robust Framework of Chromosome Straightening with ViT-Patch GAN

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