Abstract: There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.

Abstract: There is provided a method, comprising feeding a medical image into a detector component trained on a first training dataset of medical images annotated with ground truth boxes depicting a visual finding, obtaining boxes each associated with a respective box score indicative of likelihood of the visual finding, converting each respective box into a respective patch, feeding patches into a patch classifier trained on a second training dataset that includes patches extracted from the ground truth box labels of the first training dataset, wherein a patch score for a patch corresponds to a box score obtained from a box corresponding to the patch, obtaining patch scores indicative of likelihood of the visual finding being depicted, and computing a dot product of the box scores and the patch scores, and providing the dot product as an image-level indication of likelihood of the visual finding being depicted in the medical image.