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Related Concept Videos

Imaging Studies I: Kidney, Ureter, and Bladder Studies01:28

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...
Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

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Kidney Tumor Classification on CT images using Self-supervised Learning.

Erdal Özbay1, Feyza Altunbey Özbay2, Farhad Soleimanian Gharehchopogh3

  • 1Department of Computer Engineering, Firat University, 23119, Elazig, Turkey.

Computers in Biology and Medicine
|May 14, 2024
PubMed
Summary

A novel deep learning model, Self-supervised learning with Self-distillation for Kidney Tumor Detection (SSLSD-KTD), achieves high accuracy in classifying kidney tumors, aiding radiologists in diagnosis.

Keywords:
CT imagesClassificationKidney tumorMasked autoencoderSelf-supervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Kidney tumors are a significant global health concern, necessitating accurate and efficient diagnostic methods.
  • Traditional manual detection of kidney tumors is time-consuming, labor-intensive, and costly.
  • Deep learning (DL) offers a promising avenue for automated and accurate kidney tumor detection (KTD).

Purpose of the Study:

  • To develop a more effective DL model for assisting physicians in kidney tumor diagnosis.
  • To reduce the workload of radiologists and minimize diagnostic errors.
  • To improve the accuracy and efficiency of kidney tumor classification.

Main Methods:

  • Proposed a masked autoencoder (MAE) for kidney tumor detection.
  • Implemented self-supervised learning (SSL) with self-distillation (SD) for enhanced feature extraction.
  • Developed the SSLSD-KTD method, utilizing local and global attention mechanisms in its encoder and decoder.

Main Results:

  • SSLSD-KTD achieved 98.04% accuracy on the KAUH-kidney dataset and 82.14% on the CT-kidney dataset.
  • Transfer learning enhanced accuracy to 99.82% and 95.24% on the respective datasets.
  • The method demonstrated effective feature extraction from limited data.

Conclusions:

  • The SSLSD-KTD method shows significant potential for aiding or even replacing radiologists in kidney tumor diagnosis.
  • The approach is effective in extracting kidney tumor features, even with limited datasets.
  • This AI-driven tool can improve diagnostic accuracy and efficiency in oncology.