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Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with
P Kiran Rao1,2, Subarna Chatterjee2, M Janardhan3
1Artificial Intelligence, Department of Computer Science and Engineering, Ravindra College of Engineering for Women, Kurnool 518001, India.
Abstract:
Kidney tumors represent a significant medical challenge, characterized by their often-asymptomatic nature and the need for early detection to facilitate timely and effective intervention. Although neural networks have shown great promise in disease prediction, their computational demands have limited their practicality in clinical settings. This study introduces a novel methodology, the UNet-PWP architecture, tailored explicitly for kidney tumor segmentation, designed to optimize resource utilization and overcome computational complexity constraints. A key novelty in our approach is the application of adaptive partitioning, which deconstructs the intricate UNet architecture into smaller submodels. This partitioning strategy reduces computational requirements and enhances the model's efficiency in processing kidney tumor images. Additionally, we augment the UNet's depth by incorporating pre-trained weights, therefore significantly boosting its capacity to handle intricate and detailed segmentation tasks. Furthermore, we employ weight-pruning techniques to eliminate redundant zero-weighted parameters, further streamlining the UNet-PWP model without compromising its performance. To rigorously assess the effectiveness of our proposed UNet-PWP model, we conducted a comparative evaluation alongside the DeepLab V3+ model, both trained on the "KiTs 19, 21, and 23" kidney tumor dataset. Our results are optimistic, with the UNet-PWP model achieving an exceptional accuracy rate of 97.01% on both the training and test datasets, surpassing the DeepLab V3+ model in performance. Furthermore, to ensure our model's results are easily understandable and explainable. We included a fusion of the attention and Grad-CAM XAI methods. This approach provides valuable insights into the decision-making process of our model and the regions of interest that affect its predictions. In the medical field, this interpretability aspect is crucial for healthcare professionals to trust and comprehend the model's reasoning.
Insights
A new UNet-PWP architecture efficiently segments kidney tumors using adaptive partitioning and pre-trained weights. This method achieves 97.01% accuracy, outperforming DeepLab V3+ and offering explainable AI insights for clinical use.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Pathology
Background:
- Kidney tumors pose diagnostic challenges due to asymptomatic presentation, necessitating early detection.
- Neural networks show promise for disease prediction but often face computational limitations in clinical settings.
Purpose of the Study:
- Introduce the UNet-PWP architecture for efficient kidney tumor segmentation.
- Address computational complexity constraints of neural networks in medical imaging.
- Enhance model performance and interpretability for clinical application.
Main Methods:
- Developed UNet-PWP architecture with adaptive partitioning to create smaller submodels.
- Augmented UNet depth using pre-trained weights for improved segmentation capabilities.
- Applied weight-pruning techniques to optimize the model and incorporated attention and Grad-CAM XAI for interpretability.
Main Results:
- UNet-PWP achieved 97.01% accuracy on training and test datasets, surpassing DeepLab V3+.
- Adaptive partitioning and weight pruning streamlined the model without performance degradation.
- Explainable AI methods provided insights into model predictions, enhancing clinical trust.
Conclusions:
- The UNet-PWP architecture offers an efficient and accurate solution for kidney tumor segmentation.
- The model's interpretability is crucial for its adoption by healthcare professionals.
- This approach demonstrates the potential of optimized neural networks in clinical diagnostics.
