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Updated: Jan 15, 2026

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

Computer-Aided Diagnosis for Breast Ultrasound Imagery Dataset.

Tristan P Hansen¹, Jeffrey S Baggett², Richard L Ellis³

¹Mayo Clinic Health System, La Crosse, WI, USA. hansen.tristan@mayo.edu.

Journal of Imaging Informatics in Medicine

|January 13, 2026

Summary

The Computer-Aided Diagnosis for Breast Ultrasound Imaging (CADBUSI) dataset advances machine learning for breast cancer detection. It links ultrasound images with pathology results to improve diagnostic accuracy and reduce biopsies.

Keywords:

BI-RADS Breast cancer Breast ultrasound Computer-aided diagnosis Machine learning Medical imaging dataset

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Published on: August 14, 2012

Area of Science:

Medical Imaging
Machine Learning
Oncology

Background:

Breast cancer diagnosis relies heavily on imaging, with ultrasound being a key modality.
Standardization and large-scale, labeled datasets are crucial for developing robust AI diagnostic tools.
Existing datasets may lack comprehensive annotations or clinical context for advanced machine learning.

Purpose of the Study:

To introduce the Computer-Aided Diagnosis for Breast Ultrasound Imaging (CADBUSI) dataset.
To provide a large-scale, standardized dataset for machine learning in breast cancer diagnosis.
To facilitate the development of AI tools for improved breast cancer detection and reduced unnecessary biopsies.

Main Methods:

Curated 79,281 breast ultrasound exams from 60,688 patients.
Included 756,315 images and 136,197 videos with BI-RADS® assessments and pathology-verified diagnoses.
Employed a processing pipeline including Faster R-CNN text extraction, automated region detection, Noise2Noise inpainting, and HIPAA-compliant anonymization.

Main Results:

The dataset contains ground truth labels for 79,281 unique breasts (68,645 benign, 10,636 malignant).
Data is classified by malignancy presence per breast, suitable for multiple instance learning.
Standardized images and linked radiological-pathological data enhance clinical relevance.

Conclusions:

The CADBUSI dataset addresses critical challenges in ultrasound image standardization and clinical data linkage.
It enables the development of advanced computer-aided diagnostic tools for breast cancer.
Potential to significantly improve breast cancer detection accuracy and clinical decision-making.