Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 27, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Enhancing Whole Slide Image Classification in Renal Cell Carcinoma via Swin Transformer-Based Multiple Instance

Bohan Zhang¹, Zhen Gao¹

¹Faculty of Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada.

Bioengineering (Basel, Switzerland)

|June 26, 2026

Summary

This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Seconds-scale exfoliation of high-quality 2D crystals enabled by polycyclic aromatic hydrocarbon radical anion-mediated organoalkali intercalation.

Nature communications·2026

Same author

Characterization of sexually acquired HIV-1 transmission networks and genetic variation in northern frontier China, 2021-2024.

Frontiers in public health·2026

Same author

What do cancer patients discuss online regarding CINV management? A social media-based topic modeling study.

Frontiers in oncology·2026

Same author

Vactosertib reverses ABCG2-mediated multidrug resistance through dual inhibition of drug efflux and transporter expression.

Experimental cell research·2026

Same author

High-throughput identification of bacterial β-glucuronidase inhibitors using machine learning.

Gut microbes·2026

Same author

Universal transcriptomic hallmarks of mammalian ageing and mortality.

Nature·2026

Same journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026

Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026

Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026

Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026

Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026

Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026

See all related articles

Swin-CLAM, a new AI model, accurately classifies renal cell carcinoma (RCC) subtypes using whole-slide images. This method enhances diagnostic capabilities for clear cell, papillary, and chromophobe RCC, improving accuracy in cancer subtyping.

Area of Science:

Digital pathology
Computational oncology
Artificial intelligence in medicine

Background:

Renal cell carcinoma (RCC) has distinct histologic subtypes impacting prognosis and treatment.
Accurate subtyping of RCC is crucial for effective patient management.
Current classification methods can be complex and time-consuming.

Purpose of the Study:

To evaluate a weakly supervised, slide-level AI model for classifying RCC subtypes.
To assess the performance of Swin-CLAM, a modified CLAM model using Swin-Tiny Transformer, for RCC subtype classification.
To investigate the utility of advanced AI in improving the accuracy of renal cancer subtyping.

Main Methods:

Utilized 928 whole-slide images (WSIs) from TCGA-RCC for clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC) subtypes.

Keywords:

CLAM computational pathology histopathology classification multiple instance learning renal cell carcinoma swin transformer whole slide images

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jun 27, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Employed Swin-CLAM, integrating a Swin-Tiny Transformer patch encoder with the CLAM-SB aggregation module.

WSIs were processed into 256x256 patches and classified using slide-level labels in a five-fold cross-validation.

Main Results:

Swin-CLAM achieved a macro-averaged AUC of 0.976, accuracy of 94.8%, and macro-F1 of 0.940 on the TCGA-RCC dataset.
The model demonstrated the largest performance improvement for chromophobe RCC classification.
Qualitative analyses included attention heatmaps and t-SNE plots for exploratory insights.

Conclusions:

Stronger patch-level representations, like those from Swin-Tiny Transformers, enhance CLAM-based RCC subtype classification.
The proposed Swin-CLAM model shows significant potential for automated RCC subtyping.
Further validation, calibration, and domain-shift analysis are necessary for clinical implementation.