Ge Precision Healthcare LLC

Rajesh Kumar Tamada

Junpyo Hong

Ravi Soni

Venkata Ratnam Saripalli

Hans Krupakar

Attila Márk Rádics

Masaki Ikuta

Guarav Kumar

Techniques are provided for compressing deep neural networks using a structured filter pruning method that is extensible and effective. According to an embodiment, a computer-implemented method comprises determining, by a system operatively coupled to a processor, importance scores for filters of layers of a neural network model previously trained until convergence for an inferencing task on a training dataset. The method further comprises removing, by the system, a subset of the filters from one or more layers of the layers based on the importance scores associated with the subset failing to satisfy a threshold importance score value. The method further comprises converting, by the system, the neural network model into a compressed neural network model with the subset of the filters removed.

Deep neural network compression based on filter importance

Computer processing techniques are described for augmenting computed tomography (CT) images with synthetic artifacts for artificial intelligence (AI) applications. According to an example, a computer-implemented method can include generating, by a system comprising a processor, synthetic artifact data corresponding to one or more CT image artifacts, wherein the synthetic artifact data comprises anatomy agnostic synthetic representations of the one or more CT image artifacts. The method further includes generating, by the system, augmented CT images comprising the one or more CT image artifacts using the synthetic artifact data. In one or more examples, the method can further include training, by the system, a medical image inferencing model to perform an inferencing task using the augmented CT images as training images.

Artifact-driven data synthesis in computed tomography

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Bengaluru, India

Rajesh Kumar Tamada