1. Artificial Neural Networks applications
in Computer Aided Diagnosis. System
design and use as an educational tool.
Jorge Hernández Rodríguez
Education in the Knowledge Society PhD Program
Hernández Rodríguez, Jorge; Rodríguez Conde, María José; Cabrero Fraile, Francisco Javier
Track 16: Doctoral Consortium

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INDEX
● Context and motivation that drives the dissertation research
● State of the art
● Hypothesis and problem statement
● Research objective and goals
● Research approaches and methods
● Results to date and their validity. Dissertation status.
● Current and expected contributions
Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
CONTEXT AND MOTIVATION THAT DRIVES THE DISSERTATION RESEARCH (I)
Computer Aided
Detection and
Diagnosis (CAD)
Software and algorithms (image
processing, lesion detection
and classification algorithms)
“Second opinion” for the
radiologist
CADe CADx
Software specialized in
lesion and pathological
features detection
Software specialized in
pathology characterization ,
classification or diagnosis
Radiological image
modalities
Computed Tomography
Mammography
Conventional Radiology
Magnetic Resonance Imaging
Ultrasound
…

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
CONTEXT AND MOTIVATION THAT DRIVES THE DISSERTATION RESEARCH (II)
CAD UTILITY
● Increasing number of high technology equipment in hospitals
and clinics
● Radiological examinations with a high number of images and
rising number of patients scanned (CT, MRI, screening,
interventional radiology…)
● Growth of clinical indications for different types of modalities
¡ Huge amount of
workload for the
radiologist!
CAD: very useful tool to ease
workload and improve detection
and diagnosis and therefore
posterior treatment prescription.
Reported in scientific and technical literature:
► The utility of these systems has been confirmed in numerous articles
► Reduction of inter-observer variability associated with image interpretation
► Sensitivity and specificity improvement associated with its use
► Improvement in specialists’ Receiver Operating Characteristic curves
► They provide supplementary information for managing a problem
► They have been successfully integrated into Picture Archiving and
Communication Systems and Radiology Information Systems
OBJECTIVE
■ Assisting radiologists in
detection and diagnosis
■ Great potential for medical
specialists’ training and as an
educational tool

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
STATE OF THE ART (I)
Artificial
Intelligence
Artificial
Neural
Networks
(ANNs)

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
STATE OF THE ART (II)
Evolution of ANNs in Medical Imaging
↓
Convolutional Neural Networks (CNNs)
Neural Networks based in Deep Learning Methods
↓
Highly non-linear systems
Designed to work directly with images
Deeper architectures (higher number of layers)
Reduction in the amount of adjustable network parameters
Limited size of training and validation datasets
↓
Calculation of empirical features of segmented lesions in images (manually or automatically)
Use of a classification algorithm
(for example, linear discriminant analysis, support vector machines, artificial neural networks
“Traditional CAD schemes”

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
STATE OF THE ART (III): Mammography
Arévalo et al. (2016).
Computers and Methods in Biomedicine
CNNs based in Deep learning techniques
Mammography database: BCDR
Preprocessing
• Image cropping
• Data augmentation
• Global Contrast Normalization
• Local Contrast Normalization
Sample of
marked
lesion
Supervised learning
through CNN and
classification
Architecture of the CNN that performed better
Evaluation of results
Comparison with other
state-of-the art
representations for
lesion classification and
image analysis

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
STATE OF THE ART (IV): Pulmonary node classification
Cheng et al. (2016).
Scientific reports - Nature
Deep learning
techniques
SDAE classifier achieved better results
Patterns from
classifier’s
hidden layers
Examples of
rated nodules
Shen et al. (2015). Information
processing in medical imaging Multi-scale CNNs
Classification accuracy of 86,4% on LIDC-IDRI database

9. Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
STATE OF THE ART (V): Computer-Aided Detection
Roth et.al. (2016).
IEEE Transactions on Medical Imaging
Convolutional Neural Networks
● Hierarchical two-tiered CADe system
● New approach: 2,5 D image decomposition
● Wide range of applications:
Sclerotic spine metastases
ThoracoAbdominal Lymph nodes
Colonyc polyps
● Different candidate generation algorithms
● False positive reduction schemes
Detection of sclerotic metastases Detection of lymph nodes
Influence of Random View number on performance Performance on training and testing datasets of different
approaches. Lymph node detection.

10. Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
STATE OF THE ART (VI): CAD applications in medical education
Mazurowski et al. (2010). Medical Physics Zhang et al. (2014). Medical Physics Ping et al. (2005). Academic Radiology
Hypermedia instructional program in
CAD-aided mammography training
Individually adapted computer-aided
educational system in mammography
Construction of user models.
Probability of making diagnostic errors by
radiology residents (difficulty of cases).
↓
Relation with image features
Computer models to predict locations of
false-positives in mammography
Based on previous annotations

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
HYPOTHESIS AND PROBLEM STATEMENT (I)
FIRST STAGE: CAD SYSTEM DEVELOPMENT
Creation of a CAD system for radiological image analysis
Analysis of system’s performance and efficiency in pathology detection and classification.
HYPOTHESES
► The system developed is a useful tool for assisting radiologists as a “second image reader”.
► System’s sensitivity and specificity is comparable to other published results.
► The number of false positives detected is kept in reasonable numbers, similar to published values.
► Detection and classification accuracy on different image datasets reaches reported values.
► Software is capable of extracting useful image features and information for the specialist.

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
SECOND STAGE: SYSTEM VALIDATION
Evaluation of system’s effectiveness as a clinical, educational and training tool for medicine
students and future medical specialists.
HYPOTHESES
► The CAD system designed and trained with validated image datasets produces positive effects in the
analysis and diagnosis of radiological images.
► The CAD scheme developed together with its functionalities will serve as a useful and complementary
educational tool for medicine students learning to interpret medical images and for specialists’ training in
the field of diagnostic radiology.
HYPOTHESIS AND PROBLEM STATEMENT (II)

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
RESEARCH OBJECTIVES AND GOALS (I)
MAIN OBJECTIVE
Development and validation of an ANN based CAD scheme
Extensible to different image modalities and pathologies
(pulmonary nodes in CT, polyps in CT colonography, mammography…)
With tools to access and classify the CAD generated information and
educational functionalities

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
RESEARCH OBJECTIVES AND GOALS (II)
SPECIFIC
OBJECTIVES
1. Revision of CAD schemes, models and algorithms.
2. CAD scheme design.
3. Download validated image databases and annotations.
4. Project development from neural network training.
5. Integration of ANNs in a CAD platform.
6. System validation prior to clinical use.
7. System validation for educational purposes and specialists training.

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
STAGE 1: Development and validation of an ANN based CAD scheme
CAD system’s training,
adjustment and validation
Public medical
image databases
Available for
educational, research
and software
development purposes
They contain clinical and
diagnostic information
related with the cases
and pathologies
considered
Information validated by
groups of experts and
with different methods
and techniques
Image repositories for
different modalities and
pathologies
RESEARCH APPROACHES AND METHODS (I)

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
MAMMOGRAPHY
Medical image databases
THORAX
RADIOGRAPHY
CANCER IMAGE DATASETS
(DIFFERENT IMAGE MODALITIES)
RESEARCH APPROACHES AND METHODS (II)

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
Calculation environment for
working with medical images
and neural networks
Convolutional Neural
Networks Toolbox for
artificial vision applications
Image
Processing
Toolbox
Computer vision
algorithm library
(open source code)
RESEARCH APPROACHES AND METHODS (III)
Software and calculations

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
General purpose
programming language
Package of functions dedicated for
scientific computing with PythonTM
Python library for defining, optimizing
and evaluating multidimensional array
expressions (suited for ANNs)
Python software
development environment
Possibility of performing
GPU accelerated
calculations with these
environments
RESEARCH APPROACHES AND METHODS (IV)

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
RESEARCH APPROACHES AND METHODS (V)
Validation of ANNs performance
● Split of datasets into three groups: training, validation and testing
● Monitoring network parameter’s during iterative processes
● Calculation of parameters to quantify network’s performance
● Statistical analysis of the results produced in the testing phase
● Cross-validation combining different ANNs in an ensemble
- Increase the testing samples to ensure optimal tuning (if needed)

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
RESEARCH APPROACHES AND METHODS (VI)
STAGE 2: System validation for clinical use and utility as an educational tool
● Use in clinical practice by specialists
● Training in software operation
● Analysis of cases with software and result interpretation
● Study of CAD influence on reader’s performance
● Statistical analysis of a sample of cases
● Analysis of clinical cases by radiology residents and
medicine students
● Training in software operation and educational tools
● Surveys to evaluate goals during training period
● Study of CAD influence on learning process
● Statistical analysis of surveys’ results

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
RESULTS TO DATE AND THEIR VALIDITY. DISSERTATION STATUS
● Bibliographic review in the areas of CAD and ANNs.
● Installation of software environments and function libraries.
● Download of image datasets and annotations.
● Formatting of images and preprocessing steps. Classification of cases.
● Initial design of CAD application. Initial routines and scripts.
● Selection of network architecture and layer types.

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Track 16: Artificial Neural Networks application in Computer Aided Diagnosis.
System design and use as an educational tool
CURRENT AND EXPECTED CONTRIBUTIONS
● ANN training from parameter value optimization based on annotations
● Comprehensive analysis of ANNs performance. Analysis of the influence of several architecture parameters, learning
algorithms, sample sets size and type, preprocessing steps… on it.
● Implementation of tools to allow learning through CAD use. Design of specific validation tests.
● Validation of the CAD scheme in clinical practice: interaction with the use and clinical data generated
● Educational validation derived from the use of the system by medicine students and radiology residents.
● Optimization of the system from information extracted from practical use
● Future contributions published in the next TEEM conference and journals in the field of medical imaging, computers in
medicine or radiology and medical education

23. Thank you very much for your attention!