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Related Experiment Video

Updated: Jun 28, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Deep Location Soft-Embedding-Based Network With Regional Scoring for Mammogram Classification.

Bowen Han, Luhao Sun, Chao Li

IEEE Transactions on Medical Imaging

|April 16, 2024

Summary

This summary is machine-generated.

A new deep learning network, DLSEN-RS, aids in early breast cancer detection from mammograms. It accurately locates lesions without manual segmentation, improving diagnostic accuracy and reducing computational costs.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Early breast cancer detection significantly reduces mortality.
Mammography is a key screening tool.
Deep learning-based Computer-Aided Diagnosis (CAD) enhances radiologist accuracy.

Purpose of the Study:

To develop an efficient deep learning model for mammography image classification.
To overcome limitations of existing CAD methods requiring manual segmentation and complex fusion models.
To improve tumor localization and diagnostic accuracy in mammograms.

Main Methods:

Proposed a Deep Location Soft-Embedding-based Network with Regional Scoring (DLSEN-RS).
Utilized positional embedding (PE) and aggregation pooling (AP) modules for lesion localization without bounding boxes.
Employed a single feature extractor for an end-to-end classification approach.

Main Results:

DLSEN-RS demonstrated satisfactory performance on INbreast and CBIS-DDSM datasets.
The PE and AP modules proved versatile and improved tumor localization.
Achieved competitive diagnostic accuracy compared to state-of-the-art methods.

Conclusions:

DLSEN-RS offers an efficient and accurate deep learning solution for mammography analysis.
The proposed method reduces reliance on manual annotations and complex model architectures.
This approach has the potential to lower computational overhead in CAD systems.