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Abstract #4015

Development of an Automated Deep Learning Diagnostic Platform for Medical Imaging

Jing Zhang1 and Yang Song1
1MR Research Collaboration Team, Siemens Healthineers Ltd., shanghai, China

Synopsis

Keywords: AI/ML Software, AI/ML Software

Motivation: Deep learning in medical imaging offers great potential, but accessibility for clinicians is challenging.

Goal(s): To develop an automated, user-friendly platform for seamless image preprocessing, model selection, training, evaluation, and interpretation.

Approach: Built in Python using MONAI, the platform includes features like image cropping, normalization, dataset splitting, and model selection (ResNet, DenseNet). Grad-CAM support enhances interpretability. The TCAI glioma dataset was used for testing.

Results: The platform achieved high accuracy on the TCAI dataset and is easy to use, making it accessible to clinicians without extensive technical expertise.

Impact: This platform enhances clinical accessibility to deep learning diagnostic tools, supporting high-precision diagnostics and interpretability through an intuitive interface, and reducing the technical barrier to AI in medical imaging.

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Keywords