Johns Hopkins University

Alan Yuille

Elliott Fishman

Yingda Xia

Lingxi Xie

Zhuotun Zhu

Seyoun Park

Methods, systems, and apparatuses, including computer programs for detecting pancreatic neoplasms. A method includes providing an image as an input to a first model, obtaining first output data generated by the first model based on the first model's processing of the image, the first output data representing a portion of the image that depicts a pancreas, providing the first output data as an input to a second model, obtaining second output data generated by the second model based on the second model's processing of the second input data, the second output indicating whether the depicted pancreas is normal or abnormal, providing the first output data and the second output data as an input to a third model, and obtaining third output data generated by the third model, the third output data including data indicating that the pancreas is normal or data indicating a likely location of a pancreatic neoplasm.

Early detection of pancreatic neoplasms using cascaded machine learning models

Systems, methods, and apparatus for segmenting internal structures depicted in an image. In one aspect, a method includes receiving data representing image data that depicts internal structures of a subject, providing an input data structure to a machine learning model, wherein the input data structure comprises fields structuring data that represents the received data representing the image data that depicts internal structures of the subject, wherein the machine learning model is a multi-stage deep convolutional network that has been trained to segment internal structures depicted by one or more images, receiving output data generated by the machine learning model based on the machine learning model's processing of the input data structure, and processing the output data to generate rendering data that, when rendered, a computer, causes the computer to output, for display, data that visually distinguishes between different internal structures depicted by the image data.

Abdominal multi-organ segmentation with organ-attention networks

Methods, systems, apparatus, and computer programs, for processing images through multiple neural networks that are trained to detect a pancreatic ductal adenocarcinoma. In one aspect, a method includes actions of obtaining a first image that depicts a first volume of voxels, performing coarse segmentation of the first image using a first neural network trained (i) to process images having the first volume of voxels and (ii) to produce first output data, determining a region of interest of the first image based on the coarse segmentation, performing multi-stage fine segmentation on a plurality of other images that are each based on the region of interest of the first image to generate output data for each stage of the multi-stage fine segmentation, and determining based on the first output data and the output data of each stage of the multi-stage fine segmentation, whether the first image depicts a tumor.

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Baltimore, MD, United States of America

Alan Yuille