Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer

The importance of radiomics features for predicting patient outcome is now well-established. Early study of prognostic features can lead to a more efficient treatment personalisation. For this reason new radiomics features obtained through mathematical morphology-based operations are proposed. Their study is conducted on an open database of patients suffering from Nonsmall Cells Lung Carcinoma (NSCLC). The tumor features are extracted from the CT images and analyzed via PCA and a Kaplan-Meier survival analysis in order to select the most relevant ones. Among the 1,589 studied features, 32 are found relevant to predict patient survival: 27 classical radiomics features and five MM features (including both granularity and morphological covariance features). These features will contribute towards the prognostic models, and eventually to clinical decision making and the course of treatment for patients.

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

Distributed learning across a coalition of organizations allows the members of the coalition to train and share a model without sharing the data used to optimize this model. In this paper, we propose new secure architectures that guarantee preservation of data privacy, trustworthy sequence of iterative learning and equitable sharing of the learned model among each member of the coalition by using adequate encryption and blockchain mechanisms. We exemplify its deployment in the case of the distributed optimization of a deep learning convolutional neural network trained on medical images.