Radiological Expertise
Paul Taylor
Centre for Health Informatics and Multiprofessional Education
Overview
• Exposition: Signal-Symbol problem
• Cognitive studies of radiological expertise
• Perceptual studies of radiological expertise
• Coda: Radiology and ontology
Decision Making
knowledge based computer aid for making decisions about calcifications on mammograms
Identifying terms to use in arguments
METHOD:
• 11 radiologists recorded ‘thinking aloud’ while reading 20 sets of mammograms
• Verbal reports transcribed, descriptors
extracted & grouped
– 50 descriptors
Identifying arguments
• 10 radiologists read 40 sets of mammograms on which calcifications highlighted– asked to characterize calcifications using our
descriptors– test capacity of descriptors to discriminate benign and
malignant
• Identified physical dimensions underlying the descriptors– e.g. ‘size’ underlies ‘large’ and ‘small’
• Implemented image processing measures of those dimensions
Image Processing for Certain Arguments
Benign Malignant
well defined pleomorphic homogeneous segmental big within fat similar density curvilinear with a rim
Strong
Strong
isolated variable density lucent centre branching 1-5 flecks variable size scattered ill-defined vascular linear no finding clustered in skin towards nipple larger cluster few specks adjacent
Weak
oval/round
Weak
Implementing image processing
Size Variation AreaSD >= 0.0749 variable size AreaSD < 0.0749 similar size
72%
Density Variation
d = 0.126*ProminenceSD-0.997 d<-0.20485 variable density d>0.36265 similar density
45%
• Use discriminant analysis to derive classification rules
• Some rules work quite well, others less so
Evaluation of the prototype
• For purposes of the evaluation count the arguments to obtain a diagnosis
Actual diagnosisMalignant Benign
Malignant 5 8CADMIUM 2Benign 1 7
6 cases produced ties (5 benign and 1 malignant)
Conclusions
• Radiologists vary in the cues that they use to identify and characterise abnormalities
• Radiological terms may not map to a consistent definition in terms of ranges on physical characteristics– small calcifications are not always smaller
than large calcifications
Cognitive Studies of Radiological Expertise
• Radiology– Lesgold et al. 1988– Azevedo et al. 1998– Raufaste, Raufaste et Eyrolle 1998
• Others have looked at analogous specialties– Pathology
• Crowley et al. 2003
– Dermatology• Kulatunga-Moruzi, Brooks and Norman 2004
Cognitive Studies of Radiological Expertise
• Novices fail – to invoke appropriate schema– to test appropriate schema– to follow the schema through
• Intermediates able to identify features but fail to achieve diagnostic closure
• Experts – Generate hypotheses– perform longer chains of reasoning involving better integrated
clusters of findings– better able to switch schema
• All groups– mix forward and backward reasoning
Kulatunga-Moruzi et al. Cognition and Perception in Dermatology
Visual after verbal
Given comprehensive and accurate description
of image features
Asked to make diagnosis
Given photograph of case
Asked to make diagnosis
Visual
Given photograph of case
Asked to make diagnosis
Verbal
Given comprehensive and accurate description
of image features
Asked to make diagnosis
Kulatunga-Moruzi et al. Cognition and Perception in Dermatology
striped shading = dermatologist; solid shading = family practitioner; open shading = resident
Lesgold et al: Perceptual Differences
• Asked to draw key feature• Experts agree on size and
position• Half residents did not identify a
region that even approximately matched experts– their interpretations account
for an abnormality different to that perceived by experts
Perceptual and the Visual Hierarchy
Striate cortex, simple cells
Striate cortex, complex cells
Inferotemporal cortex
Anterior Inferotemporal cortex
Perceptual Learning and the Visual Hierarchy
Learning specific toorientation and location
Learning generalised across orientation, location and form
Reverse Hierarchy TheoryAhisser and Hochstien 2004
• Experiments show that: – more demanding task conditions result in
more stimulus-specific learning • Ergo, in difficult conditions learning occurs at lower
levels
– when tasks are interleaved, learning on harder tasks only occurs after learning on easier
• Ergo, learning at higher levels has to happen first
Reverse Hierarchy TheoryAhisser and Hochstien 2004
First glimpse able to access high level representationsHigh-level learning generalises over stimulus parameters
Later scrutiny accesses feature information from low level processingLow-level learning is more parameter specific
Reverse Hierarchy TheoryAhisser and Hochstien 2004
• Expert perception is immediate, holistic– Suggesting high level
representations
• Expert perception does not generalise– Based on low level
changes
Sowden, Davies and Roling (2000)
• Radiologists better than non-radiologists at detecting dots placed at random against a complex background – radiologists’ conceptual knowledge
irrelevant
• Limited experience leads to increase in the performance of novices– improvement did not transfer to
images in which the contrast was reversed
Mello-Thoms at al. Eye movement studies
• Phase 1:– Track readers’ gaze while they
inspect image
• Phase 2– Readers indicate where
abnormalities lie on image
• Responses classed by decision outcome– True Positive
• Lesion identified correctly
– False Negative• Missed lesion
– False Positive• Normal region incorrectly
labelled as containing a lesion
– True Negative• Region inspected at Phase 1 but
no lesion indicated in Phase 2
Two phases of search
• Relationship between accuracy and time to decision is different for each group
• Experts seem perceptually more sensitive, better tuned vlsual recognition
Can we identify image features that predict decision outcomes?
• Bank of filters sensitive to scale and orientation
• Compute:– Local features
• a vector of log(energy) of response across spatial frequency bands for each inspected region
– Global features• For each reader compute the mean
vector for all inspected regionsWavelet Packets
Can we identify image features that predict decision outcomes?
Can we identify image features that predict decision outcomes?
• Spatial frequency signature, combining local and global features predicts decision outcome– ANN identified 79% of TP decisions
• Visual saliency is observer-dependent– Observers attracted to different but similar areas, but
make different decisions– Observers respond differently to spatial frequency– Personal profile guides each radiologist
Overall Conclusions
• Expertise - in part - depends on perceptual learning– Training should involve extensive practice– Learning may be enhanced if easy cases
learnt first
• Salience is observer-dependent– Individuals’ representations of normal
background differ
Copyright ©Radiological Society of North America, 2005
Haller, S. et al. Radiology 2005;236:983-989
Haller and Radue 2005
• Captured fMRI scans of radiologists and non-radiologists carrying out a simple decision task – on X-ray images – on electron microscopy
images
Copyright ©Radiological Society of North America, 2005
Haller, S. et al. Radiology 2005;236:983-989
Difference between radiology and other images
in non-radiologists’ brains in radiologists’ brains
Copyright ©Radiological Society of North America, 2005
Difference between radiologists’ and non-radiologists’ brains on
non-radiology images
Haller and Radue: conclusions
• X-rays excite regions in the radiologists’ brains not activated in non-radiologists’– bilateral middle and inferior temporal gyrus, bilateral medial and
middle frontal gyrus, and left superior and inferior frontal gyrus– associated with visual attention and memory retrieval
• X-rays automatically attract attention, invoke memories
• control images invoke different patterns of activity in the two groups, – radiological expertise leads to the development of general visual
processing skills, • E.g. enhanced capacity of mentally rotating visual stimuli
Overall Conclusions
• Expertise - in part - depends on perceptual learning– Training should involve extensive practice– Learning may be enhanced if easy cases
learnt first
• Salience is observer-dependent– Individuals’ representations of normal
background differ
Radiology and Ontology
• Various initiatives require the development of ontologies or of controlled terminologies for biomedical images– RadLex– Birads– RITI– SNOMED-CT– DICOM
Difficult to describe what we see
• Design Criteria for Ontologies– Ontologies communicate the intended
meaning of defined terms– Definitions of terms should be objective
• Some applications require annotations at a level of granularity for which an objective definition of terms may not be possible
Examples of terms from RadLex• Findings
– Visual Features• Finding Related Features
– Shape» Lobular» Nodular» Pedunculated
– Margins» Poorly-defined
– Morphology» Blunted» Eroded
“These terms describe features on the image that can be described without reference to specific physical, anatomic, or pathological processes or structures”
Oh, and another thing
• Perception may be an important element of expertise in other specialties
• Doctors may need to learn to perceive all manner of clinical signs
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