Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images

Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with XAI on CT Scan Images

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 limite...

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Bibliographic Details
Main Authors: P. Kiran Rao, Subarna Chatterjee, M. Janardhan, K. Nagaraju, Surbhi Bhatia Khan, Ahlam Almusharraf, Abdullah I. Alharbe
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Diagnostics
Subjects:
adaptive partitioning
explainable AI
kidney tumor segmentation
optimization
weight pruning
UNet-PWP
Online Access:https://www.mdpi.com/2075-4418/13/20/3244

Internet

https://www.mdpi.com/2075-4418/13/20/3244

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