Techniques for improving artificial intelligence-based base calling are disclosed. The improved techniques can be used to better train artificial intelligence for base calling by reordering of sequencing images, and training of a neural network-based base caller where the temporal logic is effectively “frozen” (or bypassed). In addition, the improved techniques include various combinations, including, for example, combining “normalization” of sequencing images with reordering of sequencing images and/or with effectively “freezing” the temporal logic.

The present disclosure discloses a method for image rendering. The method may include obtaining first image data by a first processing device; performing a first rendering operation on the first image data; and determining second image data based on the first image data. The method may further include receiving a non-rendering operation on the second image data by the second processing device; and performing a second rendering operation on the non-rendered second image data.

The present disclosure discloses a method for image rendering. The method may include obtaining first image data by a first processing device; performing a first rendering operation on the first image data; and determining second image data based on the first image data. The method may further include receiving a non-rendering operation on the second image data by the second processing device; and performing a second rendering operation on the non-rendered second image data.

Computer-implemented methods and systems for identifying corresponding slices in medical image data sets are provided. For example, the systems and methods are based on identifying corresponding slices by systematically quantifying image similarities between the slices comprised in one medical image data set and the slices comprised in another medical image data set.

A registration means for storing an image of a product as a registration image in association with information representing the passing sequence that the product passed through an upstream side process; a management means for managing the matching sequence in a downstream side process; and a matching means for performing matching between an image of a product carried into the downstream side process and the registration image according to the matching sequence, are included. Each time the matching means succeeds in matching, the management means updates the matching sequence to sequence in which registration images not having succeeded in matching with any matching image are put in order on the basis of the passing sequence that the products passed through the upstream side process.

A registration means for storing an image of a product as a registration image in association with information representing the passing sequence that the product passed through an upstream side process; a management means for managing the matching sequence in a downstream side process; and a matching means for performing matching between an image of a product carried into the downstream side process and the registration image according to the matching sequence, are included. Each time the matching means succeeds in matching, the management means updates the matching sequence to sequence in which registration images not having succeeded in matching with any matching image are put in order on the basis of the passing sequence that the products passed through the upstream side process.

A novel system and method for accurate detection and quantification of fibrous tissue produces a virtual medical image of tissue treated with a second stain based on a received medical image of tissue treated with a first stain using a computer-implemented trained deep learning model. The model is trained to learn the deep texture patterns associated with collagen fibers using conditional generative adversarial networks to detect and quantify fibrous tissue.

A novel system and method for accurate detection and quantification of fibrous tissue produces a virtual medical image of tissue treated with a second stain based on a received medical image of tissue treated with a first stain using a computer-implemented trained deep learning model. The model is trained to learn the deep texture patterns associated with collagen fibers using conditional generative adversarial networks to detect and quantify fibrous tissue.

The disclosure relates to a lane line determination method and system, a vehicle, and a storage medium, and the lane line determination method includes: capturing a road image for a current location in a vehicle coordinate system; recognizing the road image and generating basic lane lines for the current location; extracting map lane lines for the current location; mapping the map lane lines to the vehicle coordinate system to obtain auxiliary lane lines; registering the basic lane lines with the auxiliary lane lines, the registration being performed based on confidence levels of the basic lane lines; and generating target lane lines based on the registered auxiliary lane lines and the basic lane lines. According to the lane line determination method, an operation such as correction can be performed, according to a condition, on a visually captured lane line by using a map lane line, thereby improving accuracy of the lane line detection.

The disclosure relates to a lane line determination method and system, a vehicle, and a storage medium, and the lane line determination method includes: capturing a road image for a current location in a vehicle coordinate system; recognizing the road image and generating basic lane lines for the current location; extracting map lane lines for the current location; mapping the map lane lines to the vehicle coordinate system to obtain auxiliary lane lines; registering the basic lane lines with the auxiliary lane lines, the registration being performed based on confidence levels of the basic lane lines; and generating target lane lines based on the registered auxiliary lane lines and the basic lane lines. According to the lane line determination method, an operation such as correction can be performed, according to a condition, on a visually captured lane line by using a map lane line, thereby improving accuracy of the lane line detection.