DISEASE DETECTION FROM WEAKLY ANNOTATED VOLUMETRIC MEDICAL IMAGES USING CONVOLUTIONAL LONG SHORT-TERM MEMORY AND MULTIPLE INSTANCE LEARNING

摘要

Systems and methods for developing a disease detection model. One method includes training the model using an image study and an associated disease label mined from a radiology report. The image study including a sequence of a plurality of two-dimensional slices of a three-dimensional image volume, and the model including a convolutional neural network layer and a convolutional long short-term memory layer. Training the model includes individually extracting a set of features from each of the plurality of two-dimensional slices using the convolutional neural network layer, sequentially processing the features extracted by the convolutional neural network layer for each of the plurality of two-dimensional slices using the convolutional long short-term memory layer, processing output from the convolutional long short-term memory layer for each of the plurality of two-dimensional slices to generate a probability of the disease, and updating the model based on comparing the probability to the label.