International Business Machines Corporation

Guerbet

Paul Dufort

Giovanni John Jacques Palma

Arkadiusz Sitek

Omid Bonakdar Sakhi

Pedro Luis Esquinas Fernandez

Murray A Reicher

Dana Levanony

Yi-Qing Wang

Thomas Binder

Amin Katouzian

Yiting Xie

Benedikt Werner Graf

Yusuke Takeuchi

A lesion detection ensemble machine learning model architecture comprising a plurality of trained machine learning (ML) computer models is provided. A first decoder of a lesion detection ML model processes a medical image input to generate a first lesion mapping prediction. A second decoder of the lesion detection ML model processes the medical image input to generate a second lesion mapping prediction. Combinational logic combines the first and second lesion mapping predictions to generate a combined prediction. Final lesion mapping output logic generates a final lesion prediction based on the combined lesion mapping prediction. The final lesion mapping output logic outputs the final lesion prediction for further downstream computing operations. The first decoder is trained with a first loss function that is configured to counterbalance a training of the second decoder that is trained using a second loss function different from the first loss function.

Ensemble machine learning model architecture for lesion detection

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 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.

A computer-implemented method for classifying and presenting a contrast phase (CP) of a contrast enhanced computerized tomography (CECT) scan is provided. The method includes training an artificial intelligence (AI) algorithm utilizing a set of CPs labeled CECT data to associate a set of characteristics of the data with a probability associated with the CP. The method includes receiving a new set of unlabeled CECT data, and applying the AI algorithm to the new unlabeled CECT data to associate a first probability of a first CP and a second probability of a second CP. The method also includes providing a graphical representation including the first probability of the first CP and the second probability of the second CP.

Method of determining contrast phase of a computerized tomography image

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a knockout autoencoder engine for detecting anomalies in biomedical images. The mechanism trains a neural network to be used as a knockout autoencoder that predicts an original based on an input image. The knockout autoencoder engine provides a biomedical image as the input image to the neural network. The neural network outputs a probability distribution for each pixel in the biomedical image. Each probability distribution represents a predicted probability distribution of expected pixel values for a given pixel in the biomedical image. An anomaly detection component executing within the knockout autoencoder engine determines a probability that each pixel has an expected value based on the probability distributions to form a plurality of expected pixel probabilities. The anomaly detection component detects an anomaly in the biomedical image based on the plurality of expected pixel probabilities. An anomaly marking component executing within the knockout autoencoder engine marks the detected anomaly in the biomedical image to form a marked biomedical image and outputs the marked biomedical image.

Knockout autoencoder for detecting anomalies in biomedical images

A system for determining a region of interest in an image. The system includes a memory and an electronic processor. The electronic processor included in the system is connected to the memory and is configured to initialize internal states of nodes of a spatial lattice. Each node of the spatial lattice corresponds to a pixel of the image and is connected to at least one node representing a neighboring pixel of the image. The electronic processor is also configured to iteratively update, using a neural network, the internal states of each nodes in the spatial lattice using spatially gated propagation and identify the region of interest within the image based on the internal states of the nodes at a convergence of the spatial lattice.

Segmenting irregular shapes in images using deep region growing

A system for determining a region of interest in an image. The system includes a memory and an electronic processor. The electronic processor is configured to create an image pyramid for the image. The image pyramid includes a plurality of layers. For each layer of the image pyramid, the electronic processor is configured to initialize internal states of nodes of a spatial lattice and iteratively update the internal states of the nodes using spatially gated propagation. Each node in the spatial lattice represents a block of pixels in the image and is connected to a node representing a neighboring block of pixels in the image. The electronic processor is also configured to identify the region of interest based on the internal states of the nodes at a convergence of the spatial lattice having nodes representing values included in a first layer of the image pyramid.

Segmenting irregular shapes in images using deep region growing with an image pyramid

Systems and techniques are disclosed for determination of lesions identified in medical images of differing perspectives. One of the methods includes obtaining images of a breast of a patient, each image being from a perspective, wherein the images include at least a first image and a second image. A skin contour line representing an obliquity of a chest wall of the patient is determined. A posterior nipple line extending from a nipple included in the first image is determined that perpendicularly intersects with the skin contour line. An interactive user interface is presented that includes the posterior nipple line on the first image. Compatibility information for lesions indicated in each of the first and second image is determined, the information indicating whether a first lesion in the first image is a same lesion in the breast as a second lesion in the second image.

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Toronto, Canada

Paul Dufort