IV — Interventional, Lab-Based & Video
19
Digital Pathology & Histology
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
19.1
What is whole-slide imaging?
19.2
Why and when it’s ordered
19.3
What diagnoses are made from it
19.4
How it works in a modern hospital
19.5
The data landscape
19.6
The model landscape
19.7
FDA-cleared AI products
19.8
Open challenges
19.9
The agentic outlook
19.10
Further reading
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IV — Interventional, Lab-Based & Video
19
Digital Pathology & Histology
19
Digital Pathology & Histology
Note
Draft chapter — outline only. Content coming soon.
19.1
What is whole-slide imaging?
19.2
Why and when it’s ordered
19.3
What diagnoses are made from it
19.4
How it works in a modern hospital
19.5
The data landscape
19.6
The model landscape
19.7
FDA-cleared AI products
19.8
Open challenges
19.9
The agentic outlook
19.10
Further reading
18
Surgical & Endoscopic Video
20
The Remaining Map