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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 V: Intravenous Urography and Retrograde Pyelography01:22

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IntroductionIntravenous Urography (IVU) and Retrograde Pyelography (RP) are important diagnostic imaging techniques used to evaluate the urinary system. These methods help identify structural abnormalities, obstructions, and functional issues in the kidneys, ureters, and bladder. Both procedures use iodine-based contrast media to enhance the visibility of urinary tract structures on X-ray images, though they differ in their methods and indications.1. Intravenous Urography (IVU)Intravenous...
Imaging Studies VI: Voiding Cystourethrography and Cystography01:22

Imaging Studies VI: Voiding Cystourethrography and Cystography

Voiding Cystourethrography (VCUG) and Cystography are specialized radiographic procedures used to examine the structure and function of the bladder and urethra.Voiding Cystourethrography (VCUG)A Voiding Cystourethrogram (VCUG) is a diagnostic imaging procedure that assesses the anatomy and function of the lower urinary tract. It focuses on the bladder, bladder neck, and urethra, helping detect abnormalities such as vesicoureteral reflux (VUR)—the backward or reverse flow of urine into the...
Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...

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Related Experiment Video

Updated: May 10, 2026

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens
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RIVA: An Image Dataset of Conventional Pap Smear Cytology with Multiple Independent Annotations.

Paula Perez Bianchi1, Sol Anselmo2, Malena Vásquez Currié2

  • 1Departamento de Computación (DC), Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires (UBA), Buenos Aires, Argentina.

Scientific Data
|December 9, 2025
PubMed
Summary

This study introduces RIVA, a new dataset of conventional Pap smear images for cervical cancer screening. The high-quality, expert-annotated dataset supports AI development for improved diagnostics.

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Oncology

Background:

  • Conventional Pap smears are crucial for cervical cancer screening, especially in low-resource settings.
  • Existing image datasets predominantly use liquid-based preparations, limiting AI development for conventional smears.
  • There is a need for high-quality, annotated datasets of conventional Pap smear images.

Purpose of the Study:

  • To introduce RIVA, a novel, high-resolution image dataset of conventional Pap smears.
  • To provide a robust ground truth for training and evaluating AI models for cervical cancer detection.
  • To enable analysis of inter-annotator consistency in cervical cytology classification.

Main Methods:

  • Collected 959 conventional Pap smear images (1024x1024 px) at 40x magnification from 115 patients.
  • Annotated images using the Bethesda classification system with up to four independent medical professionals.
  • Detailed annotations include nuclei coordinates and classification labels for 15,949 unique cells.

Main Results:

  • The RIVA dataset comprises 26,158 annotations across eight Bethesda categories, including precancerous lesions (SCC, HSIL, ASCH, LSIL, ASCUS) and non-lesion types (NILM, ENDO, INFL).
  • Achieved high inter-annotator agreement: 94% for lesion vs. non-lesion and 74% for the full eight-category scheme.
  • The dataset provides a consensus-driven ground truth for AI model development.

Conclusions:

  • RIVA is a valuable resource for advancing AI in cervical cancer screening using conventional Pap smears.
  • The dataset's quality and detailed annotations facilitate robust AI training and validation.
  • High inter-annotator agreement validates the dataset's reliability for AI development and research.