Advancing Histopathological Diagnostics: Hybrid GAN Architectures and Metrics-Based Synthesis for High-Fidelity Image Generation

Advancing Histopathological Diagnostics: Hybrid GAN Architectures and Metrics-Based Synthesis for High-Fidelity Image Generation

 

Summary

 The proposed research initiative is situated at the cutting-edge intersection of artificial intelligence and histopathological diagnostics. It seeks to address a pivotal challenge in medical imaging: the need for high-quality, diverse datasets that are essential for the accurate interpretation and diagnosis of diseases. Traditional methods of data augmentation are limited by their propensity to introduce overfitting and reduce inter-sample variability, thereby constraining the potential of machine learning models to generalize from training data to real-world applications. This team's dual-aimed approach is designed to overcome these limitations by leveraging Generative Adversarial Networks (GANs) for the synthesis of high fidelity digital pathology images from multimodal inputs, and by pioneering a novel metrics to-image generative model that translates quantifiable microstructural metrics into precise histological imagery.

Team

Himanshu Arora (Urology), Cheng-Bang Chen (Industrial and Systems Engineering)

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