III — Diagnostic Imaging, Modality by Modality
15
Mammography & Breast Imaging
AI in Medical Imaging
Preface
I — The Landscape of Medical Imaging
1
Introduction: Why AI in Medical Imaging
2
The Universe of Medical Images
3
How Images Live in a Hospital
4
Medical Image Viewers
II — Foundations: Machine Learning & Computer Vision
5
Machine Learning: The Concepts
6
What Computer Vision Can Do
7
Deep Learning Architectures
8
Generative & Frontier Models
9
Agentic AI in Medical Imaging
10
From Model to Product: Evaluation, Regulation, Deployment
III — Diagnostic Imaging, Modality by Modality
11
Chest X-ray
12
Computed Tomography (CT)
13
Magnetic Resonance Imaging (MRI)
14
Ultrasound & Echocardiography
15
Mammography & Breast Imaging
16
Nuclear Medicine: PET & SPECT
17
Ophthalmic Imaging: Fundus & OCT
IV — Interventional, Lab-Based & Video
18
Surgical & Endoscopic Video
19
Digital Pathology & Histology
20
The Remaining Map
V — Systems & The Road Ahead
21
Building an Imaging AI Platform
22
Ethics, Bias, Safety & The Future
Appendices
The Dataset Directory
The Model Zoo
FDA-Cleared AI Product Index
DICOM Quick Reference for ML Engineers
Dual Glossary
Table of contents
15.1
What is mammography?
15.2
Why and when it’s ordered
15.3
What diagnoses are made from it
15.4
How it works in a modern hospital
15.5
The data landscape
15.6
The model landscape
15.7
FDA-cleared AI products
15.8
Open challenges
15.9
The agentic outlook
15.10
Further reading
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III — Diagnostic Imaging, Modality by Modality
15
Mammography & Breast Imaging
15
Mammography & Breast Imaging
Note
Draft chapter — outline only. Content coming soon.
15.1
What is mammography?
15.2
Why and when it’s ordered
15.3
What diagnoses are made from it
15.4
How it works in a modern hospital
15.5
The data landscape
15.6
The model landscape
15.7
FDA-cleared AI products
15.8
Open challenges
15.9
The agentic outlook
15.10
Further reading
14
Ultrasound & Echocardiography
16
Nuclear Medicine: PET & SPECT