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Imaging Studies I: Kidney, Ureter, and Bladder Studies

Imaging Studies I: Kidney, Ureter, and Bladder Studies

Kidney, Ureter, and Bladder (KUB) StudiesKidney, Ureter, and Bladder (KUB) studies are standard diagnostic imaging procedures used to assess the anatomy of the urinary system. They are commonly utilized for patients experiencing abdominal pain or urinary symptoms. By using a simple X-ray of the abdomen, KUB studies can reveal structural and pathological abnormalities within the kidneys, ureters, and bladder. These studies are particularly valuable in diagnosing kidney stones, urinary...

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Optimizing Inference Distribution For Efficient Kidney Tumor Segmentation Using A Unet-pwp Deep-learning Model With Xai On Ct Scan Images.

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Optimizing Inference Distribution For Efficient Kidney Tumor Segmentation Using A Unet-pwp Deep-learning Model With Xai On Ct Scan Images.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Optimizing Inference Distribution for Efficient Kidney Tumor Segmentation Using a UNet-PWP Deep-Learning Model with

P Kiran Rao^1,2, Subarna Chatterjee², M Janardhan³

¹Artificial Intelligence, Department of Computer Science and Engineering, Ravindra College of Engineering for Women, Kurnool 518001, India.

Diagnostics (Basel, Switzerland)

|October 28, 2023

View abstract on PubMed

Summary

This summary is machine-generated.

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.

Keywords:

DeepLabV3+GCAM-attention UNet-PWP adaptive partitioning explainable AI kidney tumor segmentation optimization weight pruning

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

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.