Siamese Networks for Chromosome Classification

Swati, -; Gupta, Gaurav; Yadav, Mohit; Sharma, Monika; Vig, Lovekesh

doi:10.1109/iccvw.2017.17

Cited by 36 publications

(15 citation statements)

References 25 publications

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“…3 and as we notice we achieved better performance than [8] and [34]. Papers [4], [33], and [43] used different deep learning networks for feature extraction and classification on their own collected dataset whereas paper [39], [41], and [42] built their own CNN and used it for both feature extraction and classification on their own collected dataset. As obvious from Table 12, our classification method on the CEGMR dataset using scheme no.…”

Section: 1) Combined Dataset Resultsmentioning

confidence: 77%

“…The comparison is relative because the used techniques and datasets are different. We notice that three papers [22], [32], and [33] used U-Net semantic segmentation and paper [43] used another type of segmentation which is instance segmentation (Mask R-CNN).…”

Section: ) Comparison With the State-of-the-art Segmentation Methodsmentioning

confidence: 99%

“…Some of the researches are tending to this filed because it facilitates classifying chromosomes to 24 classes. Chromosomes are segmented manually by an expert and straightening by Swati et al [33] via medial axis extraction and crowdsourcing and via projection vectors methods. They collected the chromosome images from a hospital with 1740 images.…”

Section: Bchromosomes Classificationmentioning

confidence: 99%

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Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

2020

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Chromosome analysis is an essential task in a cytogenetics lab, where cytogeneticists can diagnose whether there are abnormalities or not. Karyotyping is a standard technique in chromosome analysis that classifies metaphase image to 24 chromosome classes. The main two categories of chromosome abnormalities are structural abnormalities that are changing in the structure of chromosomes and numerical abnormalities which include either monosomy (missing one chromosome) or trisomy (extra copy of the chromosome). Manual karyotyping is complex and requires high domain expertise, as it takes an amount of time. With these motivations, in this research, we used deep learning to automate karyotyping to recognize the common numerical abnormalities on a dataset containing 147 non-overlapped metaphase images collected from the Center of Excellence in Genomic Medicine Research at King Abdulaziz University. The metaphase images went through three stages. The first one is individual chromosomes detection using YOLOv2 Convolutional Neural Network followed by some chromosome post-processing. This step achieved 0.84 mean IoU, 0.9923 AP, and 100% individual chromosomes detection accuracy. The second stage is feature extraction and classification where we fine-tune VGG19 network using two different approaches, one by adding extra fully connected layer(s) and another by replacing fully connected layers with the global average pooling layer. The best accuracy obtained is 95.04%. The final step is detecting abnormality and this step obtained 96.67% abnormality detection accuracy. To further validate the proposed classification method, we examined the Biomedical Imaging Laboratory dataset which is publicly available online and achieved 94.11% accuracy.

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Section: 1) Combined Dataset Resultsmentioning

confidence: 77%

Section: ) Comparison With the State-of-the-art Segmentation Methodsmentioning

confidence: 99%

Section: Bchromosomes Classificationmentioning

confidence: 99%

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Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

2020

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“…(7) Swati et al [30] employ the Hausdorff L2 distance with Siamese architecture on chromosome classification, achieving a classification accuracy of around 85%. Their method compared differences between images from two sets.…”

Section: Hausdorff Distance Calculation and Chromosome Classificationmentioning

confidence: 99%

“…They tested their model on abnormal classification of 12 classes and obtained an accuracy of 97%. Swati et al [30] proposed a Siamese network taking pairs of straightened chromosomes as input. They claimed to speed up the training steps as much as 83 times quicker.…”

Section: Introductionmentioning

confidence: 99%

Extended ResNet and Label Feature Vector Based Chromosome Classification

Wang

Zhu

et al. 2020

IEEE Access

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Human chromosome classification is essential to the clinical diagnosis of cytogenetical diseases such as genetic disorders and cancer. This process, however, is time-consuming and requires specialist knowledge. Considerable efforts have been made to automat the process. Recently, methods based on Convolutional Neural Networks achieved state-of-the-art results on the chromosome classification task. Many studies used karyotype images in performance evaluation, few studies have reported the results of human chromosome classification on microscopical images. This paper proposes a novel method to classify single chromosome images into one of 24 types. In the proposed method an extended ResNet was first devised to extract features of single chromosome images. A label feature vector was then extracted for each of 24 chromosome types based on a validation dataset. Hausdorff distance between feature vector of an input image and each of 24 label feature vectors were calculated, and the label feature vector that has minimum hausdorff distance to the feature vector of the input image was selected as the potential label of the input image. To finally allocate the single chromosomes from a same microscopical image into one of 24 types, a Label Redistribution strategy was used to shrink the label space and to increase the efficiency of chromosome classification. Experiments were implemented with 90,624 single chromosome images, 644 of which were randomly picked to form a testing set in advance. The classification accuracy on microscopical images using our proposed method achieved an accuracy of 94.72%.

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ChromEDA: Chromosome classification by ensemble framework based domain adaptation

Zhang,

Fan,

Lin

et al. 2023

Microscopy Res & Technique

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The precise identification of chromosomal abnormalities based on microscopy images in clinics heavily relies on manual analysis by karyotype experts, which is expensive and labor‐intensive. Therefore, there is an urgent need for automatic classification technologies. However, current deep learning‐based methods require a large amount of annotated data to achieve satisfactory performance. Additionally, there are domain shifts between datasets obtained from different instruments and clinical agencies, which hinders the utilization of existing public data resources. To overcome these challenges, we propose a novel ensemble learning framework called ChromEDA. This method incorporates soft pseudo‐label learning, adversarial learning, and angle classification learning strategies to mitigate the domain shift. To evaluate its performance, we conducted cross‐domain classification experiments using both public and private datasets. The results demonstrate that ChromEDA effectively addresses the domain shift issue and outperforms existing methods in cross‐domain chromosome classification tasks.Research Highlights The research transferred an unsupervised cross‐domain algorithm into the chromosome microscopic image processing area, which can support fast and efficient deployment in equipment such as microscopies. To our knowledge, it is the first domain adaption application in chromosome image analysis. The research designed an ensemble learning framework‐based chromosome classification model to alleviate the domain shift between different domains. It is a combination of soft pseudo‐label learning, adversarial learning, and self‐supervised angle recognition, which can significantly reduce the annotation requirements for chromosomal image processing. The results find that for chromosomes sharing the same centromere position category, banding information may contribute more to within‐group classification. Thus, banding pattern information is encouraged to be considered for more precise classification of chromosomes.

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Siamese Networks for Chromosome Classification

Cited by 36 publications

References 25 publications

Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

Automated System for Chromosome Karyotyping to Recognize the Most Common Numerical Abnormalities Using Deep Learning

Extended ResNet and Label Feature Vector Based Chromosome Classification

ChromEDA: Chromosome classification by ensemble framework based domain adaptation

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