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Related Experiment Video

Updated: May 28, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Sequential Transfer Learning for Multi-Domain Breast Image Segmentation Using a Transformer-Enhanced Hybrid U-Net.

Shagufta Manzoor1, Javaria Amin2, Amad Zafar3

  • 1Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan.

Bioengineering (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces an advanced deep learning framework for accurate breast cancer detection using multimodal imaging. The novel CNN-Transformer model significantly improves segmentation accuracy across diverse datasets, outperforming traditional methods.

Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Computational Pathology
  • Oncology

Background:

  • Breast cancer remains a leading cause of mortality in women globally, necessitating improved detection systems.
  • Accurate segmentation of breast cancer in multimodal imaging is crucial for effective diagnosis and treatment planning.

Purpose of the Study:

  • To present a unified deep learning framework for breast cancer segmentation utilizing multimodal imaging (histopathology, MRI, mammogram, ultrasound).
  • To integrate Convolutional Neural Networks (CNNs) with Transformer modules for enhanced feature extraction and contextual understanding.
  • To evaluate the framework's performance across multiple public and local datasets.

Main Methods:

  • Developed an encoder-decoder architecture with Residual Blocks for feature extraction, progressively downsampling spatial dimensions.
Keywords:
diceimagingincremental learningresidualsegmentation

Related Experiment Videos

Last Updated: May 28, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

  • Incorporated a Transformer encoder in the bottleneck to capture long-range dependencies using multi-head self-attention.
  • Employed advanced training strategies including Dice and binary cross-entropy loss, gradient clipping, learning rate scheduling (ReduceLROnPlateau), early stopping, data augmentation, and incremental learning (warm-start fine-tuning).

    • Main Results:

    • Achieved high Dice scores across diverse datasets: 0.974 (BUS-UCLM), 0.975 (BUSI), 0.971 (BreastDM), 0.904 (TNBC NucleiSegmentation), and 0.982 (BCSD-2024).
    • The proposed CNN-Transformer framework demonstrated superior performance compared to classical U-Net models.
    • The model exhibited robustness and generalization capabilities due to applied data augmentation and incremental learning techniques.

    Conclusions:

    • The unified CNN-Transformer framework offers a powerful and accurate solution for multimodal breast cancer segmentation.
    • The integration of Transformer modules effectively enhances the model's ability to understand global and local contextual information.
    • This approach holds significant potential for improving the accuracy and reliability of computer-aided breast cancer detection systems.