artificial neural networks applications in computer aided diagnosis. system design and use as an...

Artificial Neural Networks applications in Computer Aided Diagnosis. System design and use as an educational tool.

Jorge Hernández RodríguezEducation 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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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 TomographyMammography

Conventional RadiologyMagnetic Resonance Imaging

Ultrasound…

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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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STATE OF THE ART (I)

Artificial Intelligence

Artificial Neural

Networks(ANNs)

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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 imagesDeeper architectures (higher number of layers)

Reduction in the amount of adjustable network parametersLimited 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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STATE OF THE ART (III): Mammography

Arévalo et al. (2016). Computers and Methods in Biomedicine

CNNs based in Deep learning techniquesMammography 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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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


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 metastasesThoracoAbdominal Lymph nodesColonyc 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.


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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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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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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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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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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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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MAMMOGRAPHY

Medical image databases

THORAX RADIOGRAPHY

CANCER IMAGE DATASETS (DIFFERENT IMAGE MODALITIES)

RESEARCH APPROACHES AND METHODS (II)

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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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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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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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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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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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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

Thank you very much for your attention!