Efficient aggregation method and system for multiple instance learning

A system comprising:

• at least one memory storing programmed instructions;
• one or more Input/Output (I/O) interfaces;
• and one or more hardware processors operatively coupled to the at least one memory, wherein the one or more hardware processors are configured by the programmed instructions to:
• receive a plurality of Whole Slide Images (WSIs) corresponding to a plurality of subjects;
• generate a plurality of non-overlapping patch instances from each of the plurality of WSIs, wherein a background associated with each of the plurality of non-overlapping patch instances were removed;
• extract a plurality of features from the plurality of patch instances associated with each of the plurality of WSI, using a Deep Neural Network (DNN);
• compute a plurality of feature embeddings based on the extracted plurality of features using a Machine Learning (ML) model;
• and
• train a transformed-based Multiple Instance Learning (MIL) model based on the plurality of feature embeddings associated with each of the plurality of patch instances by:
• generating a first triple vector comprising a Query (Q), a Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings;
• computing a first plurality of correlation scores between each feature embedding from among the plurality of feature embeddings with other plurality of feature embeddings from among the plurality of feature embeddings based on the first triple vector;
• selecting a first optimal feature embedding from among the plurality of feature embeddings based on the first plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the optimal feature embedding;
• generating a second triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the first optimal feature embedding;
• computing a second plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the first optimal feature embedding based on the second triple vector;
• selecting a second optimal feature embedding from among the plurality of feature embeddings based on the second plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the second optimal feature embedding;
• generating a third triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the second optimal feature embedding;
• computing a third plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the second optimal feature embedding based on the third triple vector;
• and
• selecting a third optimal feature embedding from among the plurality of feature embeddings based on the third plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the third optimal feature embedding.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving a plurality of Whole Slide Images (WSIs) corresponding to a plurality of subjects;
• generating a plurality of non-overlapping patch instances from each of the plurality of WSIs, wherein a background associated with each of the plurality of non-overlapping patch instances were removed;
• extracting a plurality of features from the plurality of patch instances associated with each of the plurality of WSI, using a Deep Neural Network (DNN);
• computing a plurality of feature embeddings based on the extracted plurality of features using a Machine Learning (ML) model;
• and
• training a transformed-based Multiple Instance Learning (MIL) model based on the plurality of feature embeddings associated with each of the plurality of patch instances by:
• generating a first triple vector comprising a Query (Q), a Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings;
• computing a first plurality of correlation scores between each feature embedding from among the plurality of feature embeddings with other plurality of feature embeddings from among the plurality of feature embeddings based on the first triple vector;
• selecting a first optimal feature embedding from among the plurality of feature embeddings based on the first plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the optimal feature embedding;
• generating a second triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the first optimal feature embedding;
• computing a second plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the first optimal feature embedding based on the second triple vector;
• selecting a second optimal feature embedding from among the plurality of feature embeddings based on the second plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the second optimal feature embedding;
• generating a third triple vector comprising the Query (Q), the Value (V), and a Key (K) by performing linear projection on the plurality of feature embeddings and the second optimal feature embedding;
• computing a third plurality of correlation scores between each feature embedding from among the plurality of feature embeddings and the second optimal feature embedding based on the third triple vector;
• and
• selecting a third optimal feature embedding from among the plurality of feature embeddings based on the third plurality of correlation scores, wherein the feature embedding with maximum correlation score is selected as the third optimal feature embedding.