MS-NAS: Multi-scale Neural Architecture Search for Medical Image Segmentation

Yan, Xingang; Jiang, Weiwen; Shi, Yiyu; Zhuo, Cheng

doi:10.1007/978-3-030-59710-8_38

Cited by 42 publications

(33 citation statements)

References 11 publications

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“…Other pseudo-3D CNN model uses adjacent slices as 3D input, but 2D convolution kernels as reported in 48,49 or 3D CNN model, which uses volume as inputs and 3D convolutions by Labonte et al 31 and an associated uncertainty metric 50 can be further investigated. There are also some emerging automatic techniques 51,52 searching optimal CNN architecture that could be potentially deployed in our current cases. Future direction might be to train a versatile network with a larger dataset for a specific collection of material.…”

Section: Discussionmentioning

confidence: 99%

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

Decencière

Nguyen

et al. 2022

npj Comput Mater

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The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.

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Section: Discussionmentioning

confidence: 99%

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

Decencière

Nguyen

et al. 2022

npj Comput Mater

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“…Such techniques are already being applied for medical image analysis. For example, Yan et al developed MS-NAS (Multi-Scale Neural Architecture Search for Medical Image Segmentation) and applied it to outperform several state-of-the-art algorithms used for segmentation of CT images [63]. Given the temporal dynamics and acquisition complexities of ultrasound data, a priori hypotheses are unlikely to arrive at efficient network structures.…”

Section: Future Steps and Upcoming Advancementsmentioning

confidence: 99%

Machine Learning in Point of Care Ultrasound

Sonko

Arnold

Кузнецов

2022

pocus

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When a patient presents to the ED, clinicians often turn to medical imaging to better understand their condition. Traditionally, imaging is collected from the patient and interpreted by a radiologist remotely. However, scanning devices are increasingly equipped with analytical software that can provide quantitative assessments at the patient’s bedside. These assessments often rely on machine learning algorithms as a means of interpreting medical images.

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“…Neural Architecture Search (NAS) has achieved state-of-the-art performance in various perceptual tasks, such as image classifications [22,23], inference security [2] and image segmentation [20].…”

Section: Neural Architecture Searchmentioning

confidence: 99%

Uncertainty Modeling of Emerging Device based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search

Yan

Juan

et al. 2021

Proceedings of the 26th Asia and South Pacific Design Automation Conference

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Emerging device-based Computing-in-memory (CiM) has been proved to be a promising candidate for high-energy efficiency deep neural network (DNN) computations. However, most emerging devices suffer uncertainty issues, resulting in a difference between actual data stored and the weight value it is designed to be. This leads to an accuracy drop from trained models to actually deployed platforms. In this work, we offer a thorough analysis of the effect of such uncertainties-induced changes in DNN models. To reduce the impact of device uncertainties, we propose UAE, an uncertaintyaware Neural Architecture Search scheme to identify a DNN model that is both accurate and robust against device uncertainties.

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MS-NAS: Multi-scale Neural Architecture Search for Medical Image Segmentation

Cited by 42 publications

References 11 publications

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

Machine Learning in Point of Care Ultrasound

Uncertainty Modeling of Emerging Device based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search

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