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

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Efficient Axillary Lymph Node Detection Via Two-stage Spatial-information-fusion-based CNN.

Ziyi Liu¹, Deqing Huang¹, Chunmei Yang²

¹Institute of Systems Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China.

Computer Methods and Programs in Biomedicine

|June 30, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning model for non-invasive axillary lymph node (ALN) metastasis detection in breast cancer patients using CECT images. The model accurately localizes and classifies ALN, outperforming existing methods.

Keywords:

Axillary lymph node metastasis CECT image Convolutional neural network Lesion location

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate preoperative diagnosis of axillary lymph node (ALN) metastasis is crucial for breast cancer patient management.
Current imaging techniques often lack the precision required for reliable ALN metastasis prediction.

Purpose of the Study:

To develop an automated deep learning scheme for non-invasive localization and classification of ALN metastasis using contrast-enhanced computed tomography (CECT) images.
To enhance the accuracy of ALN metastasis prediction through novel deep learning architectures.

Main Methods:

A two-stage deep learning strategy employing a novel detection neural network.
Integration of a convolutional block attention module for spatial information extraction.
Utilization of a bottleneck feature fusion module for multi-scale feature integration.

Main Results:

The proposed convolutional neural network (CNN) model achieved a mean Average Precision (mAP) of 0.454, significantly outperforming Faster R-CNN, YOLOv3, and EfficientDet.
For classification alone, the model demonstrated superior performance with an accuracy of 0.968, positive predictive value of 0.972, and specificity of 0.983.
The model successfully incorporated ALN localization, a capability absent in many existing prediction models.

Conclusions:

A supervised deep learning method effectively detects ALN in CECT images, confirming the benefits of spatial information and novel module integration.
The proposed model excels in both ALN localization and metastasis classification, achieving top performance across key metrics.
This approach offers a promising non-invasive tool for preoperative assessment of ALN metastasis in breast cancer.