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Understanding Novel Response Patterns of Immunotherapies

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.

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Mask uncertainty regularization to improve machine learning based medical image segmentation

Vladimir Groza [1], Benoit Huet [1], Nozha Boujemaa [1] - Affiliations : [1] Median Technologies, Valbonne, France

Poster presented at the IEEE International Symposium on Biomedical Imaging (ISBI) | April 3-7, 2020 | Virtual conference.

Segmentation of the different body structures on CT and MRI scans remains a challenging problem that requires accurate ground truth (GT) segmentation. One of the important aspects is the lack of reliability caused by radiologists annotation disagreement coupled with insufficient quality of the medical images. An independent multiple annotation is needed to overcome frequent disagreements between radiologists decisions mostly on the organs border, which is always blurred and affects on the providing an accurate segmentaKon even on contrasted CT/MRI images. Augmentation techniques aim at increasing model generalization by manipulating inputs in order to enrich set of the variability of images in regards to their GT. We introduce a method to regularize the models learning process by augmenting the information only in the associated GT masks.

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