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Immunotherapies display novel response patterns that affect the design of imaging based studies and the subsequent evaluation of imaging data. Applying traditional chemotherapy-based response assumptions to immunotherapy trials can result in inaccurate interpretation of response, premature therapy termination, and unnecessary removal of subjects from a trial. Our unique solutions for medical image analysis and management and iBiopsy® for imaging phenotyping, together with our global team of experts, are advancing the development of new drugs and diagnostic tools to monitor disease and assess response to therapy.

Pierre Baudot, PhD [1], Mathieu Bernardi, MscI[1] -Affiliations: [1] Median Technologies, Valbonne, France, in col. with Daniel Bennequin, Monica Tapia, Jean-Marc Goaillard. Corresponding author e-mail: pierre.baudot@mediantechnologies.com

Poster presented at the Geometric Sciences of Information 2019 (GSI2019) conference | Aug 27-29, 2019 |Toulouse (France). Information cohomology is a branch of topological data analysis that allows us to quatify directly statistical dependencies and independences in a given dataset. Theorems establish Shannon entropy as a first cohomology class and mutual information as coboundaries on finite probability space endowed with a random variable chain complex structure. We present some simplicial subcase applications to supervised and unsupervised learning in different contexts: transcriptomic and digits and medical CT image classification.