Deep-Learning and HPC to Boost Biomedical Applications for Health (DeepHealth)

Caballero, Miranda; Gómez, Jon Ander; Bantouna, Aimilia

doi:10.1109/cbms.2019.00040

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…HPC is widespread for the training of learning models in US processing; for example, for the localisation of common carotid artery transverse section through RCNN [JGB + 20], automatic segmentation of the carotid artery and internal jugular veins [GVV + 20], fetal standard planes recognition [PLLZ21], and segmentation and classification of anatomical structures [PBA + 19]. HPC and cloud computing also poses new challenges in terms of reorganisation of the medical analysis pipeline, where the computational demand is shifted to centralised hardware resources with a real-time execution of the network's prediction on local devices [CGB19].…”

Section: Discussionmentioning

confidence: 99%

Fast Learning Framework for Denoising of Ultrasound 2D Videos and 3D Images

Cammarasana

Nicolardi

Patanè

2022

Lecture Notes in Computer Science

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This paper proposes a novel deep-learning framework for super-resolution ultrasound images and videos in terms of spatial resolution and line reconstruction. To this end, we up-sample the acquired low-resolution image through a vision-based interpolation method; then, we train a learning-based model to improve the quality of the up-sampling. We qualitatively and quantitatively test our model on different anatomical districts (e.g., cardiac, obstetric) images and with different up-sampling resolutions (i.e., 2X, 4X). Our method improves the PSNR median value with respect to SOTA methods of 1.7% on obstetric 2X raw images, 6.1% on cardiac 2X raw images, and 4.4% on abdominal raw 4X images; it also improves the number of pixels with a low prediction error of 9.0% on obstetric 4X raw images, 5.2% on cardiac 4X raw images, and 6.2% on abdominal 4X raw images.The proposed method is then applied to the spatial super-resolution of 2D videos, by optimising the sampling of lines acquired by the probe in terms of the acquisition frequency. Our method specialises trained networks to predict the high-resolution target through the design of the network architecture and the loss function, taking into account the anatomical district and the up-sampling factor and exploiting a large ultrasound data set. The use of deep learning on large data sets overcomes the limitations of vision-based algorithms that are general and do not encode the characteristics of the data. Furthermore, the data set can be enriched with images selected by medical experts to further specialise the individual networks. Through learning and high-performance computing, the proposed super-resolution is specialised to different anatomical districts by training multiple networks. Furthermore, the computational demand is shifted to centralised hardware resources with a real-time execution of the network's prediction on local devices.

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Section: Discussionmentioning

confidence: 99%

Fast Learning Framework for Denoising of Ultrasound 2D Videos and 3D Images

Cammarasana

Nicolardi

Patanè

2022

Lecture Notes in Computer Science

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“…This article describes work undertaken in the context of the Deep-Health project, "Deep-Learning and HPC to Boost Biomedical Applications for Health" (https://deephealth-project.eu/) which has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 825111 [13]. This work has been partially supported by the HPC4AI project http: //www.hpc4ai.it [6].…”

Section: Acknowledgmentmentioning

confidence: 99%

StreamFlow: cross-breeding cloud with HPC

Colonnelli,

Cantalupo,

Merelli

et al. 2020

Preprint

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Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and batch clusters. We present a novel approach to workflow execution, called StreamFlow, that complements the workflow graph with the declarative description of potentially complex execution environments, and that makes it possible the execution onto multiple sites not sharing a common data space. StreamFlow is then exemplified on a novel bioinformatics pipeline for single-cell transcriptomic data analysis workflow.

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