Congratulations to Jiaxin Yang for passing his Qualifying Exam! His thesis proposal is titled “Green U-Shaped Learning for Medical Image Analysis: Methodologies and Applications.” His Qualifying Exam Committee members include Jay Kuo (Chair), Justin Haldar, Peter Beerel, Vasileios Magoulianitis, and Michael Khoo (Outside Member).
Artificial intelligence (AI) has rapidly transformed medical imaging by enabling more accurate, efficient, and personalized diagnosis and treatment. In particular, AI-driven medical image segmentation plays a critical role in clinical decision-making for diseases such as prostate cancer and renal cell carcinoma. However, most existing deep learning–based segmentation models rely heavily on backpropagation and large-scale computation, making them energy-intensive, difficult to interpret, and challenging to deploy in resource-constrained clinical settings.
To address these limitations, this work introduces Green U-shaped Learning (GUSL), a novel feed forward machine learning framework for 3D medical image segmentation without backpropagation. GUSL is designed to be efficient, interpretable, and environmentally sustainable, while maintaining competitive segmentation performance.
The proposed framework adopts cascaded multi-stage segmentation strategies tailored to different anatomical tasks. For prostate segmentation, a two-stage coarse-to-fine approach first localizes the prostate gland and then refines its boundaries, effectively mitigating severe class imbalance and anatomical variability, as shown in Figure 1. For kidney and kidney tumor segmentation, a progressive multi-stage cascade dynamically resizes and crops task-specific regions of interest, enabling the model to focus on anatomically relevant structures and improve segmentation accuracy, as shown in Figure 2.
Extensive experiments across multiple prostate and kidney datasets demonstrate that GUSL achieves state-of-the-art performance in prostate and kidney organ segmentation, and competitive results in kidney tumor and mass segmentation. Beyond accuracy, GUSL consistently shows substantial reductions in model size, computational cost (FLOPs), energy consumption, and carbon footprint, highlighting its advantages over conventional deep learning approaches.
These results position GUSL as a generic, interpretable, and environmentally sustainable segmentation framework, with strong potential for large-scale medical imaging applications and real-world clinical deployment, particularly in settings with limited computational resources.
