Abstract: Embodiments may include novel techniques to improve detection of objects in images, for example, in Digital Breast Tomography and that are applicable to ensembles of detectors. For example, a method may comprise generating a plurality of candidate bounding boxes for each of a plurality of image slices of imaged tissue, each generated candidate bounding box having a probability score, collecting at least some of the generated candidate bounding boxes for each slice, generating a heat map of the filtered candidate bounding boxes and filtering the candidate bounding boxes in the heat map based on a first threshold of values in the heat map, performing Non-Maximum Suppression on the heat map filtered candidate bounding boxes, and outputting at least one bounding box based on the probability score.

Abstract: Embodiments may include novel techniques to improve detection of objects in images, for example, in Digital Breast Tomography and that are applicable to ensembles of detectors. For example, a method may comprise generating a plurality of candidate bounding boxes for each of a plurality of image slices of imaged tissue, each generated candidate bounding box having a probability score, collecting at least some of the generated candidate bounding boxes for each slice, generating a heat map of the filtered candidate bounding boxes and filtering the candidate bounding boxes in the heat map based on a first threshold of values in the heat map, performing Non-Maximum Suppression on the heat map filtered candidate bounding boxes, and outputting at least one bounding box based on the probability score.

Abstract: Embodiments of the present systems and methods may provide techniques that are applicable to a variety of imaging modalities and that utilize prior knowledge of the dynamics of a physiological system to analytically generate augmented samples for machine learning. For example, in an embodiment, a method implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise generating a transform for an image of tissue based on deformation of the tissue under compression, obtaining an image of tissue using an imaging modality, and generating an output image by transforming the image of the tissue using the transform.

Abstract: Embodiments of the present systems and methods may provide techniques that are applicable to a variety of imaging modalities and that utilize prior knowledge of the dynamics of a physiological system to analytically generate augmented samples for machine learning. For example, in an embodiment, a method implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise generating a transform for an image of tissue based on deformation of the tissue under compression, obtaining an image of tissue using an imaging modality, and generating an output image by transforming the image of the tissue using the transform.