The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Cancilla, Michele; Canalini, Laura; Bolelli, Federico; Allegretti, Stefano; Carrión, Salvador; Paredes, Roberto; Gómez, Jon Ander; Leo, Simone; Piras, Marco; Pireddu, Luca; Badouh, Asaf; Marco-Sola, Santiago; Alvarez, Lluc; Moretó, Miquel; Grana, Costantino

doi:10.1109/icpr48806.2021.9411954

Cited by 15 publications

(14 citation statements)

References 9 publications

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“…In the end, each health/biomedical/clinical research team will have to make a choice between these different levels of cloud services depending on its availability of technical staff (computational biologists, data scientists, statisticians, bioinformaticians, software engineers, and so on), the computer literacy and involvement of the biomedical researchers and the clinicians, the scope and extension of the projects and other constraints, including financial issues, local infrastructure, and confidentiality matters (169)(170)(171)(172)(173).…”

Section: Precision Medicine Machine Learning and Cloud Computingmentioning

confidence: 99%

Data Integration Challenges for Machine Learning in Precision Medicine

Martínez-García

Hernández‐Lemus

2022

Front. Med.

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A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.

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Section: Precision Medicine Machine Learning and Cloud Computingmentioning

confidence: 99%

Data Integration Challenges for Machine Learning in Precision Medicine

Martínez-García

Hernández‐Lemus

2022

Front. Med.

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“…The DeepHealth toolkit (DHt) is the framework of Deephealth that is developed to provide one platform for easy deployment of Deep Learning and Computer Vision applications [3]. It uses High Performance Computing, Big Data and Cloud Computing for providing off the shelf services for all Image Processing related tasks.…”

Section: Deephealth Toolkit (Dht)mentioning

confidence: 99%

Lung Nodules Segmentation with DeepHealth Toolkit

Chaudhry

Renzulli

Perlo

et al. 2022

Lecture Notes in Computer Science

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The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation. Globally, Lung cancer is one of the leading causes of death and the early detection of lung nodules is essential for the early cancer diagnosis and survival rate of patients. The goal of this study was to demonstrate the feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to precisely segment lung nodules. Experiments for lung nodules segmentation has been carried out on UniToChest using PyECVL and PyEDDL, for data pre-processing as well as neural network training. The results depict accurate segmentation of lung nodules across a wide diameter range and better accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly available as a baseline reference.

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“…The DeepHealth Toolkit [4] is an open-source DL toolkit, particularly focused on enabling easy DL adoption in the medical field. It is written in C ++ , exposes C ++ and Python APIs, and it natively supports cloud computing.…”

Section: Deephealth Toolkitmentioning

confidence: 99%

“…To illustrate our strategy we have developed an Apache Cassandra-based [17] data-loading module, which is focused on image classification and is being integrated with the DeepHealth Toolkit [4]. However, its design and architecture are of general interest and applicability.…”

Section: Introductionmentioning

confidence: 99%

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Scaling deep learning data management with Cassandra DB

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2021

2021 IEEE International Conference on Big Data (Big Data)

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Deep learning (DL) algorithms require, to be fully effective, harvesting an increasingly large amount of data. These data, typically organized as millions of small files, stress filesystems and are difficult to manage. In fact, despite the huge development of DL tools and specialized hardware, data loading pipeline for DL still lacks behind in ease of use, standardization and scalability.In this work we try to rethink the data loading pipeline, by leveraging NoSQL DBs for storing both data and metadata, making them efficiently available through the network, and allowing easier data distribution for parallel DL training. We present our open-source, Apache Cassandra-based data loader and illustrate its use and performance, which enable easy and efficient data management and decentralized data distribution for parallel learning applications.

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The DeepHealth Toolkit: A Unified Framework to Boost Biomedical Applications

Cited by 15 publications

References 9 publications

Data Integration Challenges for Machine Learning in Precision Medicine

Data Integration Challenges for Machine Learning in Precision Medicine

Lung Nodules Segmentation with DeepHealth Toolkit

Scaling deep learning data management with Cassandra DB

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