Naveen BALARAJU is an Associate Scientist with 8 years of experience applying deep learning and computer vision to medical imaging, currently advancing point-of-care ultrasound AI at Philips Research. He combines dual MS degrees in Biomedical Engineering and Data Science with hands-on experience building GAN-based image translation and colorization models, bridging clinical insight with cutting-edge generative techniques. Focused on scalable, real-world solutions, he has a track record of translating research prototypes into product-focused models for video and image analysis. Based in Bothell, WA, he is expanding into LLMs and generative AI, bringing a rare mix of biomedical domain knowledge and practical ML engineering to improve patient care.
7 years of coding experience
Bachelor of Engineering - BE, Medical Electronics, Bachelor of Engineering - BE, Medical Electronics at Ramaiah Institute Of Technology
Master of Science - MS, Data Science, Master of Science - MS, Data Science at University at Buffalo
The objective of this project is to develop and automatic Image segmentation algorithm to detect the low grade brain tumors. Recent studies have discovered that low grade gliomas are associated with the genomic subtypes that have a particular feature shape and by analyzing the feature shape we can predict which genomic subtype that was responsible for the development of the brain tumor. The conventional way to find out the genomic subtype is by making a biopsy which involves making and incision into the skull and collecting the tissue from the tumor site which involves a huge amount of risk factors. For more details we encourage you to read the referenced paper below. we have implemented Three models "U-net", "MultiRes Unet" and a model proposed by us that we have named it as "Proposed Unet Model".
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