Abstract: Disclosed herein are systems, methods and computer-program products to create synthetic immunohistochemistry (IHC) stained digital slides generated using artificial neural networks (ANNs). In some implementations, the created digital slides can be used as a ground truth to evaluate a method of analyzing IHC stained tissues.

Abstract: Disclosed herein are systems, methods and computer-program products to create synthetic immunohistochemistry (IHC) stained digital slides generated using artificial neural networks (ANNs). In some implementations, the created digital slides can be used as a ground truth to evaluate a method of analyzing IHC stained tissues.

Abstract: Disclosed herein are systems, methods and computer-program products to create synthetic immunohistochemistry (IHC) stained digital slides generated using artificial neural networks (ANNs). In some implementations, the created digital slides can be used as a ground truth to evaluate a method of analyzing IHC stained tissues.

Abstract: Disclosed herein are systems and methods to detect a wide range of eardrum conditions by using visually-descriptive words of a tympanic membrane of a subject.

Abstract: Disclosed herein are systems and methods to detect a wide range of eardrum abnormalities by using high-resolution otoscope images of both a left eardrum and a right eardrum of a subject and report the condition of each of the eardrums as “normal” or “abnormal.

Abstract: Automated image analysis methods to identify and quantify tumor buds in a high resolution image of a section of a tumor that is stained using either pan-cytokeratin AE1/3 or hematoxylin and eosin (H&E) are disclosed. The methods may be used to aid and/or replace manual visual inspection for tumor buds and may be used to predict a clinically relevant outcome or treatment in some cases. The disclosed methods may be used for many different cancer types, such as colorectal cancer.

Abstract: Disclosed herein are systems, methods and computer-program products to create synthetic immunohistochemistry (IHC) stained digital slides generated using artificial neural networks (ANNs). In some implementations, the created digital slides can be used as a ground truth to evaluate a method of analyzing IHC stained tissues.

Abstract: Automated image analysis methods to identify and quantify tumor buds in a high resolution image of a section of a tumor that is stained using either pan-cytokeratin AE1/3 or hematoxylin and eosin (H&E) are disclosed. The methods may be used to aid and/or replace manual visual inspection for tumor buds and may be used to predict a clinically relevant outcome or treatment in some cases. The disclosed methods may be used for many different cancer types, such as colorectal cancer.

Abstract: A method for refining shape estimates of detected abnormalities in medical images, particularly for the detection of pulmonary lesions in CT imagery is described. A cue point is refined prior to an initially segmentation of the lesion. A radial gradient is computed for points on an initial segmented lesion. Portions of the lesion with radial gradients deviating beyond a threshold angle are removed from the lesion. Registering imagery from more than one CT exam uses a high intensity structure, essentially bone, to provide coarse and fine alignment of a set of two-dimensional images is also described. A MIP image is formed from the three-dimensional images. A second MIP image is then formed from imagery. The second MIP image is correlated with the first MIP image to determine a preferred registration location.