Abstract: Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.

Abstract: Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.

Abstract: Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.

Abstract: Computer-implemented methods and apparatuses for anomaly detection in volumetric images are provided. A two-dimensional convolutional neural network (CNN) is used to encode slices within a volumetric image, such as a CT scan. The CNN may be trained using an output layer that is subsequently omitted during use of the CNN as an encoder. The CNN encoder output is applied to a recurrent neural network (RNN), such as a long short-term memory network. The RNN may output various indications of the presence, probability and/or location of anomalies within the volumetric image.

Abstract: The invention provides an etchant and a method of using the etchant for revealing dislocations on low index planes in group II-VI compounds such as (Hg.sub.x Cd.sub.(1-x))Te. The etchant has a composition proportional to 80 ml water, 10 ml of hydrochloric acid, 20 ml of nitric acid, and 8 gm of potassium dichromate. In another embodiment, the concentration of nitric acid is reduced to 0%. The surface of the compound being examined is exposed to the etchant for about 20 second to 6 minutes depending upon the crystallographic plane being examined. The surface is then rinsed in water. For the {111}B plane, additional rinses in a 0.05% solution of bromine in methanol and methanol are used.