Public Datasets and Techniques for Segmentation of Anatomical Structures from Chest X-Rays: Comparitive Study, Current Trends and Future Directions

Jangam, Ebenezer; Rao, A. C.

doi:10.1007/978-981-13-9184-2_29

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Table V presents the Jaccard similarity coefficient of each contour on the validation dataset. The results confirmed our method provides comparable accuracy to previous works using the JSRT dataset and the NLM(MC) dataset [27], [28].…”

Section: A Segmentation Performance On Cross-databasesupporting

confidence: 84%

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Park

2020

IEEE Trans. Med. Imaging

768

677

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Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

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Section: A Segmentation Performance On Cross-databasesupporting

confidence: 84%

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Park

2020

IEEE Trans. Med. Imaging

768

677

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show abstract

“…Table 3 presents Dice indices of lung segmentation results. Compared to both the U-Net and the XLSor of Dice index 0.976, which is currently SOTA performance of deep-learning based normal lung segmentation (Jangam and Rao, 2018), the proposed methods showed comparable Dice index in addition to providing su- perb performance for the abnormal CXR.…”

Section: Unsupervised Segmentationmentioning

confidence: 96%

CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation

Oh¹,

Ye²

2021

Preprint

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As the segmentation labels are scarce, extensive researches have been conducted to train segmentation networks without labels or with only limited labels. In particular, semi-supervised domain adaptation and self-supervised learning have been introduced to distill knowledge from various tasks to improve the segmentation performance. However, these approaches appear different from each other, so it is not clear how these seemingly different approaches can be combined for better performance. Inspired by the recent StarGANv2 for multi-domain image translation, here we propose a novel segmentation framework via AdaIN-based knowledge distillation, where a single generator with AdaIN layers is trained along with the AdaIN code generator and style encoder so that the generator can perform both domain adaptation and segmentation. Specifically, our framework is designed to deal with difficult situations in chest X-ray (CXR) segmentation tasks where segmentation masks are only available for normal CXR data, but the trained model should be applied for both normal and abnormal CXR images. Since a single generator is used for abnormal to normal domain conversion and segmentation by simply changing AdaIN codes, the generator can synergistically learn the common features to improve segmentation performance. Experimental results using CXR data confirm that the trained network can achieve the state-of-the art segmentation performance for both normal and abnormal CXR images.

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Public Datasets and Techniques for Segmentation of Anatomical Structures from Chest X-Rays: Comparitive Study, Current Trends and Future Directions

Cited by 2 publications

References 25 publications

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets

CXR Segmentation by AdaIN-based Domain Adaptation and Knowledge Distillation

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