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Microscopic Tumour Classification by Digital Mammography.

Jingjing Yang¹, Huichao Li¹, Ning Shi¹

¹Affiliated Hospital of Hebei University, Baoding, Hebei 071000, China.

Journal of Healthcare Engineering

|February 22, 2021

Summary

This summary is machine-generated.

Deep learning models accurately segment microscopic tumors in digital mammography, improving diagnostic accuracy for breast conditions. This enhances the distinction between benign and malignant findings, reducing misdiagnosis rates.

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Oncology

Background:

Traditional image segmentation methods have limitations in accurately identifying microscopic tumors.
Accurate segmentation is crucial for classifying lesions and improving diagnostic precision in mammography.

Purpose of the Study:

To investigate deep learning models for classifying microscopic tumors using digital mammography.
To compare the performance of DeepLab and Mask RCNN models for lesion segmentation.
To evaluate the diagnostic value of mammography and ultrasonography for nonspecific mastitis.

Main Methods:

Developed and compared two deep learning segmentation networks: DeepLab with void convolution and Mask RCNN with ResNet.
Utilized histopathology for ER, PR, HER, and Ki-67 scoring.
Employed Kaplan-Meier, Log-rank tests, and Cox regression for survival analysis.

Main Results:

Deep learning models demonstrated accuracy and feasibility in segmenting lesion areas in medical images.
Survival analysis identified prognostic factors affecting progression-free survival.
Comparison of imaging characteristics for nonspecific mastitis was performed.

Conclusions:

Deep learning approaches offer improved accuracy for microscopic tumor segmentation in mammography.
Accurate segmentation aids in differentiating benign from malignant breast conditions.
Enhanced understanding of imaging for nonspecific mastitis can reduce misdiagnosis.