Paola Casti

TL;DR: A multistage approach to detection and classification of mammographic lesions that is independent of accurate extraction of their contours is presented, with the ultimate goal to discriminate malignant tumors from benign lesions and normal parenchymal tissue in a realistic scenario of lesion candidates automatically detected in mammograms.

TL;DR: It is hypothesize that quantification of structural similarity or dissimilarity between paired mammographic regions can be effective in detecting asymmetric signs of breast cancer.

TL;DR: The properties of a classifier based on an ensemble of Adaptive Artificial Immune Networks (A2INET) applied to original mammography image indicators aimed at diagnosing bilateral asymmetry that is known to be correlated with increased breast cancer risk are investigated.

TL;DR: This work proposes a novel procedure for the estimation of the breast skin-line based upon multidirectional Gabor filtering that mostly outperformed the other approaches and demonstrates the effectiveness and robustness of the proposed algorithm.

TL;DR: The existence of universal features in cell motility (a so called “motility style”) which are identified by the DL approach in the rationale of discovering the unknown message in cell trajectories are demonstrated.