Abstract: A system for annotating mammography images of a subject using thermal images and mammography images of the subject using thermal and mammography images of the subject by (i) identifying a first breast region in the thermal image and a second breast region in the mammography image, (ii) identifying a block of pixels (Pt) with a high temperature region associated with a breast lesion within the first breast region, (iii) estimating a location (l) of the breast lesion corresponding to the identified block of pixels (Pt), (iv) determining a block of pixels (Pm) corresponding to the location (l) of the block of pixels (Pt) within the second breast region and (v) generating a report with an annotated mammography image with markings of a determined block of pixels (Pm) on the mammography image of the subject corresponding to the block of pixels (Pt) on the thermal image.

Abstract: A system and method for processing values of a subset of numeric features to provide interpretability of the results of a machine learning model, by determining an extent of the contribution of the subset of features towards a predicted class by performing: (i) receiving the thermal image, (ii) obtaining a region of interest in the thermal image of the subject, (iii) extracting a plurality of numeric features associated with the region of interest of the thermal image, (iv) predicting a class, (v) estimate an extent of contribution of the subset of numeric features towards the decision of the first machine learning prediction model (M) and (vi) generate a report that includes a generated discrete values that determines the extent of contribution of the subset of numeric features towards the predicted class.

Abstract: A system and method for processing values of a subset of numeric features to provide interpretability of the results of a machine learning model, by determining an extent of the contribution of the subset of features towards a predicted class by performing: (i) receiving the thermal image, (ii) obtaining a region of interest in the thermal image of the subject, (iii) extracting a plurality of numeric features associated with the region of interest of the thermal image, (iv) predicting a class, (v) estimate an extent of contribution of the subset of numeric features towards the decision of the first machine learning prediction model (M) and (vi) generate a report that includes a generated discrete values that determines the extent of contribution of the subset of numeric features towards the predicted class.