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Related Experiment Video

Updated: Jan 11, 2026

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

In Vivo, Percutaneous, Needle Based, Optical Coherence Tomography of Renal Masses

Published on: March 30, 2015

Renal Cell Carcinoma subtyping: Learning from multi-resolution localization.

Mohamad Mohamad¹, Francesco Ponzio², Santa Di Cataldo²

¹Université Côte d'Azur, INRIA, CNRS, Sophia Antipolis, France.

Computer Methods and Programs in Biomedicine

|November 9, 2025

Summary

This summary is machine-generated.

This study introduces a Self-Supervised Learning (SSL) framework for classifying renal cell carcinoma (RCC) subtypes using whole histological slide images (WSIs). The approach reduces the need for manual annotations, improving diagnostic efficiency and robustness in diverse clinical settings.

Keywords:

Digital pathology Renal cell carcinoma Self-supervised learning Whole slide images

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Area of Science:

Digital pathology
Artificial intelligence in oncology
Computational imaging

Background:

Renal Cell Carcinoma (RCC) diagnosis is often delayed, impacting treatment. Prognosis varies by subtype, necessitating accurate classification.
Current AI diagnostic tools require extensive annotated data, limiting their widespread use.
Whole Histological Slide Images (WSIs) offer rich data but present challenges in annotation and analysis.

Purpose of the Study:

To investigate a Self-Supervised Learning (SSL) framework for RCC subtype classification.
To reduce reliance on large annotated datasets for AI-driven cancer diagnosis.
To enhance the robustness and generalizability of AI models for WSIs.

Main Methods:

Developed an SSL model mimicking pathologist's multi-scale reasoning on WSIs.
Integrated information across different magnification levels within WSIs.
Validated the model's robustness and generalization using external and internal datasets with heterogeneous acquisition conditions.

Main Results:

The SSL approach achieved stable classification performance across all validation settings.
Demonstrated reduced dependence on manual data labeling.
Showcased improved robustness despite variations in image acquisition (scanners, workflows).

Conclusions:

The proposed SSL framework offers a generalizable and annotation-efficient strategy for RCC subtype classification.
SSL effectively leverages multi-resolution WSI data, overcoming annotation limitations.
This method holds potential for improving AI-assisted cancer diagnostics in real-world clinical environments.