Your address will show here +12 34 56 78
Deep Learning, Medical Imaging

Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)

By on

February 13, 2020

- Deep Learning, Medical Imaging

TCLearn is a scalable method enabling multiple partners to participate together in distributed learning. Each new iteration of the model is first validated through federated byzantine agreement to guarantee the quality of the resulting model before being recorded in a blockchain. TCLearn preserves the privacy of both the data and the model while ensuring its optimal efficiency.

Link: 10.1109/ACCESS.2019.2959220

Full abstract

Distributed learning across coalitions is becoming popular for multi-centric implementation of deep learning models. However, the level of trust between the members of a coalition can vary and requires different security architectures. Privacy of the training data has been largely described in distributed learning. In this paper, we present a scalable security architecture providing additional features such as validation on the sources quality, confidentiality on the model within a trusted coalition or confidentiality among untrusted partners inside the coalition. More specifically, we propose solutions that guarantee preservation not only of data privacy but also of data quality, enforce a trustworthy sequence of iterative learning, and that lead to equitable sharing of the learned model among the coalition’s members. We give an example of its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.

Distributed learning across coalitions is becoming popular for multi-centric implementation of deep learning models. However, the level of trust between the members of a coalition can vary and requires different security architectures. Privacy of the training data has been largely described in distributed learning. In this paper, we present a scalable security architecture providing additional features such as validation on the sources quality, confidentiality on the model within a trusted coalition or confidentiality among untrusted partners inside the coalition. More specifically, we propose solutions that guarantee preservation not only of data privacy but also of data quality, enforce a trustworthy sequence of iterative learning, and that lead to equitable sharing of the learned model among the coalition’s members. We give an example of its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.

0
Medical Imaging

Proton radiography to reduce range uncertainty in proton therapy

By on

November 15, 2018

- Medical Imaging

ImagX.R, the research group on image guided proton therapy, has the pleasure to announce the PhD defence of Sylvain Deffet. He will present his research on proton radiography and discuss how this method could improve treatment outcome in proton therapy.

Proton therapy is an advanced form of radiation therapy which is increasingly used worldwide. Unlike photons, protons deliver a sharp dose at a precise location corresponding to their range in the patient. Thanks to this physical property, proton therapy has the potential to spare healthy tissues better than conventional radiation therapy. However, a consequence of this dosimetric property is that the range of the protons inside the patient must be accurately predicted to deliver the dose as planned. Unfortunately, several uncertainties arising during treatment planning may significantly impact the range of the protons and hence jeopardize dose conformity. To better quantify and potentially reduce the uncertainties, the implementation of imaging techniques that would provide a direct information on the energy reduction of protons in the patient is highly desirable. In the present thesis, such a method is introduced: proton radiography. The system that we propose relies on the use of protons having an energy high enough for them to traverse the patient and stop inside a detector which measures their residual range. By comparing measured and predicted residual ranges, it is possible to quantify range uncertainties in the patient in clinical conditions.

Although the principle of proton radiography is relatively straightforward, a clinical implementation of such a measurement device is a complex issue. This thesis aims at developing a proton radiography system and also at conceiving an acquisition process that could be conveniently implemented in clinics. The methodologies to correctly take advantage of proton radiography measurements are also discussed, in order to further optimize the dose delivered to the patient and improve de facto the treatment outcome.
0