Arizona State University

Shiv Gehlot

Jianming Liang

Zongwei Zhou

Nahid Ul Islam

Described herein are means for systematically determining an optimal approach for the computer-aided diagnosis of a pulmonary embolism, in the context of processing medical imaging. According to a particular embodiment, there is a system specially configured for diagnosing a Pulmonary Embolism (PE) within new medical images which form no part of the dataset upon which the AI model was trained. Such a system executes operations for receiving a plurality of medical images and processing the plurality of medical images by executing an image-level classification algorithm to determine the presence or absence of a Pulmonary Embolism (PE) within each image via operations including: pre-training an AI model through supervised learning to identify ground truth; fine-tuning the pre-trained AI model specifically for PE diagnosis to generate a pre-trained PE diagnosis and detection AI model; wherein the pre-trained AI model is based on a modified CNN architecture having introduced therein a squeeze and excitation (SE) block enabling the CNN architecture to extract informative features from the plurality of medical images by fusing spatial and channel-wise information; applying the pre-trained PE diagnosis and detection AI model to new medical images to render a prediction as to the presence or absence of the Pulmonary Embolism within the new medical images; and outputting the prediction as a PE diagnosis for a medical patient.

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Darwara, India

Shiv Gehlot