International Business Machines Corporation

Guerbet

Thomas Binder

Giovanni John Jacques Palma

Arkadiusz Sitek

Dana Levanony

Yi-Qing Wang

Paul Dufort

Omid Bonakdar Sakhi

Pedro Luis Esquinas Fernandez

Frederic Commandeur

A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML model(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Lesion detection artificial intelligence pipeline computing system

A false positive removal engine is provided. The false positive removal engine receives detected objects in one or more images. A machine learning classifier computer model, configured with first operational parameters to implement a first operating point, processes the received input to classify each detected object as being a true positive or a false positive to generate a first set of object classifications. If the first set is empty, the false positive removal engine outputs the first set as a filtered list of objects; otherwise the ML classifier computer model is configured with second operational parameters to implement a second operating point, different from the first operating point, which then processes the received input to classify each detected object and generate a second set of objects classified as true positive, which is output by the false positive removal engine as the filtered list of objects.

Multiple operating point false positive removal for lesion identification

A lesion detection and classification artificial intelligence (AI) pipeline comprising a plurality of trained machine learning (ML) computer models is provided. First ML model(s) process an input volume of medical images (VOI) to determine whether VOI depicts a predetermined amount of an anatomical structure. The AI pipeline determines whether criteria, such as a predetermined amount of an anatomical structure of interest being depicted in the input volume, are satisfied by output of the first ML model(s). If so, lesion processing operations are performed including: second ML modal(s) processing the VOI to detect lesions which correspond to the anatomical structure of interest; third ML model(s) performing lesion segmentation and combining of lesion contours associated with a same lesion; and fourth ML models processing the listing of lesions to classify the lesions. The AI pipeline outputs the listing of lesions and the classifications for downstream computing system processing.

Some embodiments of the present invention select image data that are valuable for developing and/or training a deep learning based algorithm. A semi-automated system identifies cases that are the most valuable (most impactful, useful, and/or most effective) for developing and/or training the deep learning algorithm. The semi-automated system monitors a degree of uncertainty in the results produced by an image processing algorithm. Cases where the degree of uncertainty is high, and consequently a confidence score is low, are made ready for analysis, classification, and/or annotation by human review. Once analyzed, classified and/or annotated by human review, the data is made available for use in developing and/or training the deep learning algorithm.

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Paris, France

Thomas Binder