Abstract: The method includes receiving a plurality of digital images each depicting a tissue sample; splitting each of the received images into a plurality of tiles; automatically generating tile pairs, each tile pair having assigned a label being indicative of the degree of similarity of two tissue patterns depicted in the two tiles of the pair, wherein the degree of similarity is computed as a function of the spatial proximity of the two tiles in the pair, wherein the distance positively correlates with dissimilarity; and training a machine learning module—MLM—using the labeled tile pairs as training data to generate a trained MLM, the trained MLM being configured for performing an image analysis of digital histopathology images.

Abstract: In one embodiment, a method includes indexing a whole slide image dataset to generate one or more dataset embeddings corresponding to one or more respective regions of one or more whole slide images. Each dataset embedding includes a feature vector mapping the respective region to a feature embedding space. The method includes accessing a query image and generating an embedding for the query image that includes a feature vector mapping the query image to the feature embedding space. The method includes identifying result tiles by comparing the embedding for the query image to one or more of the dataset embeddings. The comparison is based on one or more distances between the embedding for the query image and the one or more of the dataset embeddings in the feature embedding space. The method includes generating a user interface including a display of the result tiles.

Abstract: Systems and methods relate to predicting disease progression by processing digital pathology images using neural networks. A digital pathology image that depicts a specimen stained with one or more stains is accessed. The specimen may have been collected from a subject. A set of patches are defined for the digital pathology image. Each patch of the set of patches depicts a portion of the digital pathology image. For each patch of the set of patches and using an attention-score neural network, an attention score is generated. The attention-score neural network may have been trained using a loss function that penalized attention-score variability across patches in training digital pathology images labeled to indicate no or low subsequent disease progression. Using a result-prediction neural network and the attention scores, a result is generated that represents a prediction of whether or an extent to which a disease of the subject will progress.

Abstract: The method includes receiving digital images of tissue samples of patients, the images having assigned a label indicating a patient-related attribute value; splitting each received image into a set of image tiles; computing a feature vector for each tile; training a Multiple-Instance-Learning program on all the tiles and respective feature vectors for computing for each of the tiles a numerical value being indicative of the predictive power of the feature vector associated with the tile in respect to the label of the tile's respective image; and outputting a report gallery including tiles sorted in accordance with their respectively computed numerical value and/or including a graphical representation of the numerical value.

Abstract: A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

Abstract: A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

Abstract: Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.

Abstract: A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

Abstract: The method includes, for each of a plurality of tiles of an image, extracting a feature vector from the tile; providing a Multiple-Instance-Learning program configured to use a model for classifying any input image as a member of one out of at least two different classes based on feature vectors extracted from the tiles; for each of the tiles, computing a certainty value indicating the certainty of the model regarding the contribution of the tile's feature vector on the classification of the image; for each of the images, using, by the MIL-program, a certainty-value-based pooling function for aggregating the feature vectors of the image or predictive values computed from the feature vectors of the image into an aggregated predictive value as a function of the certainty values of the tiles; and classifying each of the images as a member of one of the classes based on the aggregated predictive value.

Abstract: The method includes receiving a plurality of digital images each depicting a tissue sample; splitting each of the received images into a plurality of tiles; automatically generating tile pairs, each tile pair having assigned a label being indicative of the degree of similarity of two tissue patterns depicted in the two tiles of the pair, wherein the degree of similarity is computed as a function of the spatial proximity of the two tiles in the pair, wherein the distance positively correlates with dissimilarity; and training a machine learning module—MLM—using the labeled tile pairs as training data to generate a trained MLM, the trained MLM being configured for performing an image analysis of digital histopathology images.

Abstract: The method includes receiving digital images of tissue samples of patients, the images having assigned a label indicating a patient-related attribute value; splitting each received image into a set of image tiles; computing a feature vector for each tile; training a Multiple-Instance-Learning program on all the tiles and respective feature vectors for computing for each of the tiles a numerical value being indicative of the predictive power of the feature vector associated with the tile in respect to the label of the tile's respective image; and outputting a report gallery including tiles sorted in accordance with their respectively computed numerical value and/or including a graphical representation of the numerical value.

Abstract: The invention is made out of methods for the development of Deep Neural Networks for cell detection and quantification in Whole Slide Images (WSI): 1. Method to create generic cell detector that detects the centers and contours of all cells in a WSI. 2. Method to create algorithms to detect cells of specific categories and that can classify between various types of cells of different categories. 3. Method for efficient cell annotation with online learning. 4. Method for efficient cell annotation with active learning. 5. Method for efficient cell annotation with online learning and data balancing. 6. Method for auto annotation of cells 7.