PowerPoint Presentation
BD4BC: an image analysis perspectiveSir Michael Brady FRS FREng
FMedSciProfessor of Oncological ImagingDepartment of
OncologyUniversity of Oxford
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A day in the life of a clinicianBD4BC: an Image Analysis
PerspectiveHuge databases can be collected easilyInternet (2.0),
Cloud processingStatistical power, e.g. 4M mammos p/y 75M mammos
p/y rising to 150M within 10 yearsSubstantial developments in
machine learningRandom Forests, Deep Learning, Model driven
(conventional stats) Data driven priors + big dataMarginalise
confoundsHow might we learn to recognise an automobile??Given 100
examples, rely upon a model that is, tell the program the
answer
Example 1/4: Pattern Learning & RecognitionDeep learning for
Computer-Aided Detection of Mammo Lesions
Deep learning applied to 250X250 patches of 45,000
mammogramsWhat could we learn from a training set of 25,000,000
mammograms that is beyond 45,000??What could we learn from 100,000
Mammo+ABUS/MRI??? Example 1/4: Pattern Learning &
RecognitionLarge scale deep learning for CAD Mammo
LesionsPrediction of likely masking and Interval cancers1/3
interval cancers in UK were predictable on previous mammo
VDG DensityScreen detectedbreast cancerInterval breast
cancerORunadjusted95% CIORunadjusted95%
CI11(ref)1(ref)21.61*1.192.192.38*1.174.8631.64*1.202.264.77*2.389.5741.490.972.295.93*2.7412.82ORage
adjusted95% CIORage adjusted95%
CI11(ref)1(ref)21.65*1.212.242.45*1.204.9931.78*1.292.475.24*2.5910.5941.69*1.082.636.86*3.1215.11VBD
(per SD increase)XOR95% CIOR95%
CIORunadjusted1.11*1.021.221.51*1.311.75ORage
adjusted1.17*1.061.291.58*1.361.84*P
