Method and system for automatic chromosome classification

A system comprising:

• one or more data storage devices operatively coupled to one or more hardware processors and configured to store instructions configured for execution by the one or more hardware processors to:
• receive a chromosome image comprising a chromosome with a plurality of chromosome bands;
• extract visual features associated with the plurality of chromosome bands of the chromosome by generating a plurality of feature maps with dimension G×H×K;
• obtain a plurality of feature vectors from the plurality of feature maps, with each feature vector Fg of dimension H*K, by applying horizontal slicing on the plurality of feature maps;
• concatenate the plurality of feature vectors in sequence from a top chromosome band to a bottom chromosome band among the plurality of chromosome bands to generate a feature sequence;
• and
• classify the chromosome into a class label among a plurality of pre-determined class labels of chromosomes by processing the feature sequence using a Recurrent Neural Network (RNN) augmented with an attention mechanism, wherein the RNN learns the feature sequence and the attention mechanism concentrates on a plurality of Regions-of-interest (ROIs) of the feature sequence, wherein the ROIs are specific to a class label of chromosomes.

A non-transitory computer readable medium storing instructions which, when executed by a hardware processor, causes the hardware processor to perform actions comprising:

• receiving a chromosome image comprising a chromosome with a plurality of chromosome bands;
• extracting visual features associated with the plurality of chromosome bands of the chromosome by generating a plurality of feature maps with dimension G×H×K;
• obtaining a plurality of feature vectors from the plurality of feature maps, with each feature vector Fg of dimension H*K, by applying horizontal slicing on the plurality of feature maps;
• concatenating the plurality of feature vectors in sequence from a top chromosome band to a bottom chromosome band among the plurality of chromosome bands to generate a feature sequence;
• and
• classifying the chromosome into a class label among a plurality of pre-determined class labels of chromosomes by processing the feature sequence using a Recurrent Neural Network (RNN) augmented with an attention mechanism, wherein the RNN learns the feature sequence and the attention mechanism concentrates on a plurality of Regions-of-interest (ROIs) of the feature sequence, wherein the ROIs are specific to a class label of chromosomes.