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- Cossío, Fernando, et al.
(författare)
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VAI-B: a multicenter platform for the external validation of artificial intelligence algorithms in breast imaging
- 2023
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Ingår i: Journal of Medical Imaging. - : SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. - 2329-4302 .- 2329-4310. ; 10:06
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: Multiple vendors are currently offering artificial intelligence (AI) computer-aided systems for triage detection, diagnosis, and risk prediction of breast cancer based on screening mammography. There is an imminent need to establish validation platforms that enable fair and transparent testing of these systems against external data.Approach: We developed validation of artificial intelligence for breast imaging (VAI-B), a platform for independent validation of AI algorithms in breast imaging. The platform is a hybrid solution, with one part implemented in the cloud and another in an on-premises environment at Karolinska Institute. Cloud services provide the flexibility of scaling the computing power during inference time, while secure on-premises clinical data storage preserves their privacy. A MongoDB database and a python package were developed to store and manage the data on-premises. VAI-B requires four data components: radiological images, AI inferences, radiologist assessments, and cancer outcomes.Results: To pilot test VAI-B, we defined a case-control population based on 8080 patients diagnosed with breast cancer and 36,339 healthy women based on the Swedish national quality registry for breast cancer. Images and radiological assessments from more than 100,000 mammography examinations were extracted from hospitals in three regions of Sweden. The images were processed by AI systems from three vendors in a virtual private cloud to produce abnormality scores related to signs of cancer in the images. A total of 105,706 examinations have been processed and stored in the database.Conclusions: We have created a platform that will allow downstream evaluation of AI systems for breast cancer detection, which enables faster development cycles for participating vendors and safer AI adoption for participating hospitals. The platform was designed to be scalable and ready to be expanded should a new vendor want to evaluate their system or should a new hospital wish to obtain an evaluation of different AI systems on their images.
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- Fehér, Tamás, 1984, et al.
(författare)
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Simulation of runaway electron generation during plasma shutdown by impurity injection in ITER
- 2011
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Ingår i: Plasma Physics and Controlled Fusion. - 1361-6587 .- 0741-3335. ; 53:3, s. 035014-
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Tidskriftsartikel (refereegranskat)abstract
- Disruptions in a large tokamak can cause serious damage to the device and should be avoided or mitigated. Massive gas or killer pellet injection are possible ways to obtain a controlled fast plasma shutdown before a natural disruption occurs. In this work, plasma shutdown scenarios with different types of impurities are studied for an ITER-like plasma. Plasma cooling, runaway generation and the associated electric field diffusion are calculated with a 1D-code taking the Dreicer, hot-tail and avalanche runaway generation processes into account. Thin, radially localized sheets with high temperature can be created after the thermal quench, and the Dreicer and avalanche processes produce a high runaway current inside these sheets. At high impurity concentration the Dreicer process is suppressed but hot-tail runaways are created. Favorable thermal and current quench times can be achieved with a mixture of deuterium and neon or argon. However, to prevent the avalanche process from creating a significant runaway current fraction, it is found to be necessary to include runaway losses in the model.
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