Journey from rapid Experimentation to distributed training: Develop your deep learning model for cancer diagnosis

Background: Bone metastasis is one of the most frequent diseases in breast, lung and prostate cancer; bone scintigraphy is the primary imaging method of screening that offers the highest sensitivity (95%) regarding metastases. To address the considerable problem of bone metastasis diagnosis, focused on breast cancer patients, artificial intelligence methods devoted to deep-learning algorithms for medical image analysis are investigated in this research work; (2) Methods: Deep learning is a powerful algorithm for automatic classification and diagnosis of medical images whereas its implementation is achieved by the use of convolutional neural networks (CNNs). The purpose of this study is to build a robust CNN model that will be able to classify images of whole-body scans in patients suffering from breast cancer, depending on whether or not they are infected by metastasis of breast cancer; (3) Results: A robust CNN architecture is selected based on CNN exploration performance for bone metastasis diagnosis using whole-body scan images, achieving a high classification accuracy of 92.50%. The best-performing CNN method is compared with other popular and well-known CNN architectures for medical imaging like ResNet50, VGG16, MobileNet, and DenseNet, reported in the literature, providing superior classification accuracy; and (4) Conclusions: Prediction results show the efficacy of the proposed deep learning approach in bone metastasis diagnosis for breast cancer patients in nuclear medicine.
Who is this presentation for?
Machine Learning, Artificial Intelligence, Cloud Computing, Multi-Cloud, Digital Transformation, Builder, Data Science
Prerequisite knowledge:
Foundational know-how of machine learning
What you'll learn?
Key takeaways - Introduction to current state and future state needs in HealthCare and Life Sciences domain. - Business outcomes and associated technical outcomes to enable digital transformation in HCLS - Right tool for the job that allows accelerated science, boost productivity, allow reduce time to market - Technical know-how on Vertex AI: A Unified Machine Learning development platform. Vertex AI makes sophisticated AI more accessible with flexible tools for streamlined and scalable collaboration across all levels of technical expertise - Scientists and Researchers don’t need to be Engineers. Enhanced MLOps capabilities make practitioners job easier with robust, self-service workflow management.

Sarita Joshi is a Machine Learning Specialist with Google Cloud Healthcare and Life Sciences group. Sarita works with strategic customers, partners to create and execute on their healthcare transformation vision. Before joining Google Cloud, Sarita led multiple Artificial Intelligence customer transformation journeys at Amazon Web Services. She held positions including Senior Science Manager, Delivery Practice Leader for Professional Services. Sarita has several years of experience as a consultant advising clients across many industries and technical domains, including AI, ML, analytics, and SAP. She primarily held positions in Product engineering, R&D, technical consulting and business development organizations including Amazon, Accenture, Philips HealthCare, Tata Consulting, Nordstrom. She earned a master’s degree in Computer Science, Specialty Data from Northeastern University and a bachelor’s degree from Mumbai University.