Abstract: The present disclosure relates to techniques for efficient development of initial models and efficient model update and/or adaptation to a different image domain using an adaptive learning framework. For efficient development of initial models, a two-step development strategy may be performed as follows: Phase 1: Model preconditioning, where an artificial intelligence system leverages existing annotated datasets and improves learning skills through training of these datasets; and Phase 2: Target-model training, where an artificial intelligence system utilizes the learning skills learned from Phase 1 to extend itself to a different image domain (target domain) with less number of annotations required in the target domain than conventional learning methods.

Abstract: Methods, computer-program products and systems are provided to perform actions including: receiving an image and displaying the image using a graphical user interface; receiving at least one first image annotation provided by a user via the graphical user interface; producing a first segmented image using a deep learning model, wherein the deep learning model uses the digital pathology image and the at least one first image annotation; and displaying the first segmented image using the graphical user interface; receiving at least one second image annotation provided by the user via the graphical user interface; producing a second segmented image using the deep learning model, wherein the deep learning model uses the digital pathology image, the at least one first image annotation, and the at least one second image annotation; and displaying the second segmented image using the graphical user interface.

Abstract: The present disclosure relates to techniques for pre-processing training data, augmenting training data, and using synthetic training data to effectively train a machine learning model to (i) reject adversarial example images, and (ii) detect, characterize and/or classify some or all regions of images that do not include adversarial example regions. Particularly, aspects of the present disclosure are directed to receiving a training set of images for training a machine learning algorithm to detect, characterize, classify, or a combination thereof some or all regions or objects within the images, augmenting the training set of images with synthetic images generated from one or more adversarial algorithms to generate augmented batches of images, and train the machine learning algorithm using the augmented batches of images to generate a machine learning model configured to detect, characterize, classify, or a combination thereof some or all regions or objects within new images.

Abstract: Method and systems for of using a machine-learning model to detect predicted artifacts at a target image resolution are provided. A machine-learning model trained to detect artifact pixels in images at a target image resolution is accessed. An image depicting at least part of the biological sample at an initial image resolution can be converted at the target image resolution. The machine-learning model is applied to the converted image to identify one or more artifact pixels from the converted image. Method and systems for training the machine-learning model to detect predicted artifacts at the target image resolution are also provided.

Abstract: Methods and systems can include: accessing a digital pathology image; generating, using a first machine-learning model, a segmented image that identifies at least: a predicted diseased region and a background region in the digital pathology image; detecting depictions of a set of cells in the digital pathology image; generating, using a second machine-learning model, a cell classification for each cell of the set of cells, wherein the cell classification is selected from a set of potential classifications that indicate which, if any, of a set of biomarkers are expressed in the cell; detecting that a subset of the set of cells are within the background region; and updating the cell classification for each cell of at least some cells in the subset to be a background classification that was not included in the set of potential classifications.