Sarah Pendhari

I am currently pursuing Bachelors in Computer Engineering (BE) at Thadomal Shahani Engineering College at Mumbai University My primary interests lie in the intersection of Computer Vision and Machine Learning.

In the summer of 2024, I was a Research Intern at IIT Bombay, where I developed IoT-based lake monitoring systems and improved satellite imagery quality using advanced image processing techniques.

From July to September 2024, I worked as a Software Engineer Fellow at Headstarter AI, building BuzzBot, an AI-powered customer support chatbot with advanced features like multi-language support and real-time feedback.

In the summer of 2023, I interned at Wondrlab India Pvt.Ltd., optimizing EDA and web scraping pipelines to improve efficiency by 9.2%.

In January 2022, I worked as a Research Intern at M.H. Saboo Siddik College of Engineering, developing ColorViTGAN, a state-of-the-art image colorization model using Vision Transformers and CycleGAN.

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Early and accurate diagnosis of brain tumors is critical for effective treatment and improved survival rates. While MRI scans are invaluable non-invasive tools, their manual interpretation is labor-intensive due to the complexity of 3D imaging. This study leverages cutting-edge deep learning models—CNN, VGG16, VGG19, ResNet-50, MobileNet, and InceptionV3—to automate brain tumor detection, achieving remarkable accuracy scores: CNN (97.55%), VGG16 (97.96%), and InceptionV3 (97.55%) patient outcomes.

ColourViTGAN is a novel hybrid approach combining Vision Transformers (ViTs) and CycleGANs to achieve superior image recolorization for grayscale inputs. By integrating ViTs into both the generator and discriminator, the model captures long-range dependencies while preserving local details. Custom perceptual loss functions and advanced regularization techniques further enhance stability and performance. ColourViTGAN surpasses state-of-the-art models like Pix2Pix and ChromaGAN in terms of PSNR, SSIM, and LPIPS.

Early detection of microaneurysms in diabetic retinopathy (DR) is critical for preventing vision loss but remains challenging due to limited labeled data and subtle lesion features. This study introduces a few-shot learning model integrating dual attention mechanisms with prototypical networks to address these challenges. Enhanced with spatial and channel attention modules, our modified ResNet-50 backbone achieves precise localization and adaptive feature weighting.

This study addresses the challenges of color vision deficiencies, particularly tritanopia, by proposing an innovative image transformation approach to enhance color perception. Using convolutional autoencoders, our method converts blue hues into indigo shades, making them more distinguishable for individuals with tritanopia. Training on a specialized dataset derived from COCO2017, we optimize for accuracy, precision, and perceptual fidelity using TensorFlow and Keras.

Source code from Jon Barron