Abstract: The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set.

Abstract: The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set.

Abstract: The claimed subject matter relates to an architecture that can facilitate more efficient free view generation in Ray-Space by way of a Radon transform. The architecture can render virtual views based upon original image data by employing Ray-Space interpolation techniques. In particular, the architecture can apply the Radon transform to a feature epipolar plane image (FEPI) to extract more suitable slope or direction candidates. In addition, the architecture can facilitate improved block-based matching techniques in order to determine an optimal linear interpretation direction.

Abstract: The claimed subject matter relates to an architecture that can facilitate more efficient free view generation in Ray-Space by way of a Radon transform. The architecture can render virtual views based upon original image data by employing Ray-Space interpolation techniques. In particular, the architecture can apply the Radon transform to a feature epipolar plane image (FEPI) to extract more suitable slope or direction candidates. In addition, the architecture can facilitate improved block-based matching techniques in order to determine an optimal linear interpretation direction.

Abstract: The claimed subject matter relates to an architecture that can facilitate more efficient free view generation in Ray-Space by way of a Radon transform. The architecture can render virtual views based upon original image data by employing Ray-Space interpolation techniques. In particular, the architecture can apply the Radon transform to a feature epipolar plane image (FEPI) to extract more suitable slope or direction candidates. In addition, the architecture can facilitate improved block-based matching techniques in order to determine an optimal linear interpretation direction.

Abstract: The claimed subject matter relates to an architecture that can facilitate more efficient free view generation in Ray-Space by way of a Radon transform. The architecture can render virtual views based upon original image data by employing Ray-Space interpolation techniques. In particular, the architecture can apply the Radon transform to a feature epipolar plane image (FEPI) to extract more suitable slope or direction candidates. In addition, the architecture can facilitate improved block-based matching techniques in order to determine an optimal linear interpretation direction.

Abstract: An exemplary metallic frame (60) is formed by forging and precise machining of a workpiece. The forging process is performed before the precise machining process, a hardness of the frame is larger than that of the workpiece and a surface roughness Ra of the frame reaches about 6.4 microns. The present invention further provides a method of making the frame. The method includes as follows: feeding workpiece process (12) for providing a workpiece (20); forging process (14), the workpiece be forged to form a preform (40); and precise machining process (18), the preform be precisely machined to form a frame.