Pattern recognition for cache management in distributed medical imaging environments

Viana-Ferreira, Carlos; Ribeiro, Luís S.; Matos, Sérgio; Costa, Carlos

doi:10.1007/s11548-015-1272-4

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Many works on cache management has been published over the years (DBLP reports 477 matches to "cache management"), but most of the propositions are specific to certain architectures (e.g. OLAP [18], J2EE [19]), require support from the network nodes [20] or are optimised for special use cases [21]. Some require column stores [22], a middle tier [23], [24] or database support [25] [26].…”

Section: Related Workmentioning

confidence: 99%

Data Validation for Big Live Data

Crowe,

Begg,

Laux

et al. 2021

Preprint

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Data Integration of heterogeneous data sources relies either on periodically transferring large amounts of data to a physical Data Warehouse or retrieving data from the sources on request only. The latter results in the creation of what is referred to as a virtual Data Warehouse, which is preferable when the use of the latest data is paramount. However, the downside is that it adds network traffic and suffers from performance degradation when the amount of data is high. In this paper, we propose the use of a readCheck validator to ensure the timeliness of the queried data and reduced data traffic. It is further shown that the readCheck allows transactions to update data in the data sources obeying full Atomicity, Consistency, Isolation, and Durability (ACID) properties.

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Section: Related Workmentioning

confidence: 99%

Data Validation for Big Live Data

Crowe,

Begg,

Laux

et al. 2021

Preprint

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“…2) Labeller & Pattern Recognition Module: This module is responsible for detecting which usage pattern best fits the user's behavior and results from the integration of a new Labeller module within an adapted version of the Pattern Recognition mechanism proposed in [21], classifying user behavior to understand if it is relevant to prefetching mechanisms or an one-off event with little medical and health relevance.…”

Section: A Proposed Architecturementioning

confidence: 99%

An Intelligent Cloud Storage Gateway for Medical Imaging

et al. 2017

Self Cite

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Abstract-Historically, medical imaging repositories have been supported by indoor infrastructures. However, the amount of diagnostic imaging procedures has continuously increased over the last decades, imposing several challenges associated with the storage volume, data redundancy and availability. Cloud platforms are focused on delivering hardware and software services over the Internet, becoming an appealing solution for repository outsourcing. Although this option may bring financial and technological benefits, it also presents new challenges. In medical imaging scenarios, communication latency is a critical issue that still hinders the adoption of this paradigm.This paper proposes an intelligent Cloud storage gateway that optimizes data access times. This is achieved through a new cache architecture that combines static rules and pattern recognition for eviction and prefetching.The evaluation results, obtained through simulations over a real-world dataset, show that cache hit ratios can reach around 80%, leading reductions of image retrieval times by over 60%.The combined use of eviction and prefetching policies proposed can significantly reduce communication latency, even when using a small cache in comparison to the total size of the repository. Apart from the performance gains, the proposed system is capable of adjusting to specific workflows of different institutions.

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“…The rules also require maintenance to track changes over time, and therefore are difficult to use in practice. In [11], it was suggested to use a machine learning system with incremental learning to address these problems in the domain of Prefetch Prediction. It enables the system to adjust itself to user patterns, software and institutional workflow so that it becomes unnecessary to choose a priori the rules that fit each case.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, we propose to use similar dynamic machine learning system for the Preprocessing Prediction problem. However, instead of using features related to query and retrieve as was proposed in [11] for Prefetch prediction, we use DICOM (Digital Imaging and Communications in Medicine) metadata (non-imaging data) as input for the predictor. This information is incorporated in each scan, and can therefore be readily used.…”

Section: Introductionmentioning

confidence: 99%

Preprocessing Prediction of Advanced Algorithms for Medical Imaging

Fadida-Specktor

2017

J Digit Imaging

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Advanced medical imaging algorithms (such as bone removal, vessel segmentation, or a lung nodule detection) can provide extremely valuable information to the radiologists, but they might sometimes be very time consuming. Being able to run the algorithms in advance can be a possible solution. However, we do not know which algorithm to run on a given dataset before it is actually used. It is possible to manually insert matching rules for preprocessing algorithms, but it requires high maintenance and does not work well in practice. This paper presents a dynamic machine learning solution for predicting which advanced visualization (AV) algorithm needs to be applied on a given series. The system gets a handful of free text DICOM tags as an input and builds a model in the clinical setting. It incorporates a Bag of Words (BOW) feature extractor and a Random Forest classifier. The approach was tested on two datasets from clinical sites which use different languages and varying scanner models. We show that even without feature extraction, sensitivity of above 90% can be reached on both of them. By using BOW feature extractor, precision and sensitivity can usually be further improved. Even on a noisy and highly unbalanced dataset, only around 100 samples were needed to reach sensitivity of above 80% and specificity of above 97%. We show how the solution can be part of a Smart Preprocessing mechanism in a viewing software. Using such a system will ultimately minimize the time to launch studies and improve radiologists reading time efficiency.

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Pattern recognition for cache management in distributed medical imaging environments

Abstract: The proposed approach is very interesting for cache replacement and prefetching policies due to the good results obtained since the first deployment moments.

Cited by 5 publications

References 23 publications

Data Validation for Big Live Data

Data Validation for Big Live Data

An Intelligent Cloud Storage Gateway for Medical Imaging

Preprocessing Prediction of Advanced Algorithms for Medical Imaging

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