Hoffmann-La Roche Inc.

Genentech, Inc.

Ebay, Incorporated

Ventana Medical Systems, Inc.

Ido Ben-shaul

Yao Nie

Fabien Gaire

Jacob Gildenblat

Dmitry Basin

Trung Kien Nguyen

Xiao Li

Ofir Etz Hadar

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.

Attention-based multiple instance learning

A search query and a concept comprising a feature are received from a user. Positive and negative item sets are identified, where items of the positive item set comprise the feature and items of the negative item set do not comprise the feature. A hyperplane located between the positive and negative item sets is generated using a machine learning model. A concept activation vector (CAV) orthogonal to the hyperplane is determined. The CAV is used to produce a modified search query vector. Based on comparing the modified search query vector with item vectors, a second set of search results is provided.

Concept refinement using concept activation vectors

Growing community of inventors

Ramat HaSharon, Israel

Ido Ben-shaul