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From Diagnosis to Treatment: AI and Quantum Computing in Healthcare 

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COURSE OBJECTIVE

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Advanced course that uses classical artificial intelligence and quantum computing to increase the effectiveness of health diagnostics, create models for the drug development process and image classification to detect diseases early, among other applications.

 

Machine Learning (ML) in healthcare can be used by medical professionals to develop better diagnostic tools to analyze medical images. For example, a machine learning algorithm can be used on medical images (such as X-rays or MRIs) using pattern recognition to look for patterns that indicate a particular disease. This type of machine learning algorithm could help doctors make faster and more accurate diagnoses, resulting in better outcomes for patients.

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Healthcare organizations and pharmaceutical companies can also use a deep learning model to identify relevant information in data that could lead to drug discovery, development of new drugs by pharmaceutical companies, and new treatments for diseases. For example, machine learning in healthcare could be used to analyze medical data and research from clinical trials to find previously unknown side effects of drugs. This type of healthcare machine learning in clinical trials could help improve patient care, drug discovery, and the safety and effectiveness of medical procedures.

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ML in healthcare can also be used by medical professionals to improve the quality of patient care. For example, deep learning algorithms could be used by the healthcare industry to develop systems that proactively monitor patients and provide alerts to medical devices or electronic health records when changes in their status occur. This type of data collection machine learning could help ensure that patients receive the right care at the right time.

 

About ML, a module on advanced data processing is presented, explaining, among other topics: sampling, exploratory analysis, detection of outliers, advanced segmentation techniques, feature engineering and classification algorithms.

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During the course, ML and Deep Learning predictive models are shown such as: decision trees, neural networks, Bayesian networks, Support Vector Machine, ensemble model, etc. And as for neural networks, the feed forward, recurrent RNN, convolved CNN and Generative adversarial architectures are exposed. In addition, probabilistic machine learning models such as Gaussian processes and Bayesian neural networks have been included.

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Computer vision is a form of artificial intelligence (AI) and machine learning that allows computers to extract meaningful information from images and automate actions based on that information, quickly and at scale.

 

Computer vision has the ability to recognize patterns and make diagnoses in medical images with much greater precision and speed and fewer errors. It has the potential to extract information from medical images that are not visible to the human eye. Therefore, computer vision models for image classification using powerful ML models are presented in the course.

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During the course, real cases are addressed, including early detection of obesity using classical ML models and Quantum Machine Learning (QLM), identification and categorization of diabetic retinopathy using convolved neural networks, drug discovery using generative and adversarial neural networks. GAN.​

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QUANTUM COMPUTING

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Quantum Machine Learning is the integration of quantum algorithms within Machine Learning programs. Machine learning algorithms are used to calculate large amounts of data, quantum machine learning uses qubits and quantum operations or specialized quantum systems to improve the speed of calculation and data storage performed by a program's algorithms. For example, some mathematical and numerical techniques from quantum physics are applicable to classical deep learning. A quantum neural network has computational capabilities to decrease the number of steps, the qubits used, and the computing time.

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The important objective of the course is to show the use of quantum computing and tensor networks to improve the calculation of machine learning algorithms.

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Additionally, the course explains quantum computing, quantum circuits, important quantum algorithms, quantum mechanics, quantum error and correction, and quantum machine learning.

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The course explains applications of quantum computing, such as Quantum Machine Learning, in clinical and medical solutions, such as diabetes, esophageal cancer and drug discovery. The improvement of quantum models over traditional ML models is presented, for example for drug discovery a GAN neural network is used compared to its counterpart the GAN quantum neural network.

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IMPORTANT

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The great need to correctly apply traditional and quantum artificial intelligence in healthcare has forced us to include a very advanced validation module and powerful model risk techniques as well as probabilistic machine learning methodologies in order to understand the uncertainty that there is in the results. We have also included a module called XAI to prevent models from being black boxes and being interpretable.

 

WHO SHOULD ATTEND?

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The Course is aimed at healthcare professionals and laboratories interested in developing powerful artificial intelligence and quantum computing models applied to Healthcare.

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For a better understanding of the topics, it is necessary that the participant have knowledge of statistics and mathematics.​

 

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Schedules:

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  • Europe: Mon-Fri, CEST 16-20 h

 

  • America: Mon-Fri, CDT 18-21 h

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  • Asia: Mon-Fri, IST 18-21 h

 

 

 

 

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Price: 7.900 €

 

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Level: Advanced

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Duration: 36 h

 

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     Material: 

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  • Presentations PDF

  • Exercises in Excel, R, Python, Jupyterlab y Tensorflow

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AGENDA

From Diagnosis to Treatment:

AI and Quantum Computing in Healthcare 

 

Anchor 10

Machine Learning

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Module 1: Machine Learning

 

  • Definition of Machine Learning

  • Machine Learning Methodology

    • Data Storage

    • Abstraction

    • Generalization

    • Assessment

  • Supervised Learning

  • Unsupervised Learning

  • Reinforcement Learning

  • Вeep learning

  • Typology of Machine Learning algorithms

  • Steps to Implement an Algorithm

    • Information collection

    • Exploratory Analysis

    • Model Training

    • Model Evaluation

    • Model improvements

    • Machine Learning in consumer credit risk

  • Machine Learning in credit scoring models

  • Quantum Machine Learning

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Module 2: EDA Exploratory Analysis

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  • Data typology

  • Transactional data

  • Unstructured data embedded in text documents

  • Social Media Data

  • Data sources

  • Data review

  • Target definition

  • Time horizon of the target variable

  • Sampling

    • Random Sampling

    • Stratified Sampling

    • Rebalanced Sampling

  • Exploratory Analysis:

    • Histograms

    • Q Q Plot

    • Moment analysis

    • boxplot

  • Treatment of Missing values

    • Multivariate Imputation Model

  • Advanced Outlier detection and treatment techniques

    • Univariate technique: winsorized and trimming

    • Multivariate Technique: Mahalanobis Distance

  • ​Exercise 1: EDA Exploratory Analysis​

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Module 3: Feature Engineering​

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  • Data Standardization

  • Variable categorization

    • Equal Interval Binning

    • Equal Frequency Binning

    • Chi-Square Test

  • Binary coding

  • Binning

    • Kind of transformation

    • Univariate Analysis with Target variable

    • Variable Selection

    • Optimization of continuous variables

    • Optimization of categorical variables

  • Exercise 2: Detection and treatment of Advanced Outliers

  • Exercise 3: Stratified and Random Sampling in R

  • Exercise 4: Multivariate imputation model

  • Exercise 5: Univariate analysis in percentiles in R

  • Exercise 6: Continuous variable optimal univariate analysis in Excel

  • Exercise 7: Estimation of the KS, Gini and IV of each variable in Excel

  • Exercise 8: Feature Engineering of variables

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Unsupervised Learning

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Module 4: Unsupervised models

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  • Hierarchical Clusters

  • K Means

  • standard algorithm

  • Euclidean distance

  • Principal Component Analysis (PCA)

  • Advanced PCA Visualization

  • Eigenvectors and Eigenvalues

  • Exercise 9: Segmentation of the data with K-Means R

 

Supervised Learning

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Module 5: Logistic Regression and LASSO Regression

 

  • Econometric Models

    • Logit regression

    • probit regression

    • Piecewise Regression

    • survival models

  • Machine Learning Models

    • Lasso Regression

    • Ridge Regression

  • Exercise 10: Lasso Logistic Regression in R

  • Exercise 11: Ridge Regression in R

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Module 6: Trees and KNN 

 

  • Decision Trees

    • Modeling

    • Advantages and disadvantages

    • Recursion and Partitioning Processes

    • Recursive partitioning tree

    • Pruning Decision tree

    • Conditional inference tree

    • Tree display

    • Measurement of decision tree prediction

    • CHAID model

    • Model C5.0

  • K-Nearest Neighbors KNN

    • Modeling

    • Advantages and disadvantages

    • Euclidean distance

    • Distance Manhattan

    • K value selection

  • Exercise 12: KNN and PCA

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Module 7: Support Vector Machine SVM

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  • Support Vector Classification

  • Support Vector Regression

  • optimal hyperplane

  • Support Vectors

  • Add costs

  • Advantages and disadvantages

  • SVM visualization

  • Tuning SVM

  • Kernel trick

  • Exercise 14: Support Vector Machine in R

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Module 8: Ensemble Learning

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  • Classification and regression ensemble models

  • Bagging

  • Bagging trees

  • Random Forest

  • Boosting

  • Adaboost

  • Gradient Boosting Trees

  • Xgboost

  • Advantages and disadvantages

  • Exercise 15:  Boosting in R

  • Exercise 16: Bagging in R

  • Exercise 17: Random Forest, R and Python

  • Exercise 18:  Gradient Boosting Trees

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​Deep Learning

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Module 9: Introduction to Deep Learning

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  • Definition and concept of deep learning

  • Why now the use of deep learning?

  • Neural network architectures

  • Cost function

  • Gradient descending optimization

  • Use of deep learning

    • How many hidden layers?

    • How many neurons, 100, 1000?

    • How many times and size of the batch size?

    • What is the best activation function?

  • Hardware, CPU, GPU and cloud environments

  • Advantages and disadvantages of deep learning

 

Module 10: Deep Learning Feed Forward Neural Networks

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  • Single Layer Perceptron

  • Multiple Layer Perceptron

  • Neural network architectures

  • Activation function

    • Sigmoidal

    • Rectified linear unit (Relu)

    • The U

    • Selu

    • Hyperbolic hypertangent

    • Softmax

    • Other

  • Back propagation

    • Directional derivatives

    • Gradients

    • Jacobians

    • Chain rule

    • Optimization and local and global minima

  • Exercise 19: Deep Learning Feed Forward

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Module 11: Deep Learning Convolutional Neural Networks CNN

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  • CNN for pictures

  • Design and architectures

  • Convolution operation

  • Descending gradient

  • Filters

  • Strider

  • Padding

  • Subsampling

  • Pooling

  • Fully connected

  • Exercise 20: deep learning CNN

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Module 12: Deep Learning Recurrent Neural Networks RNN

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  • Natural Language Processing

  • Natural Language Processing (NLP) text classification

  • Long Term Short Term Memory (LSTM)

  • Hopfield

  • Bidirectional associative memory

  • Descending gradient

  • Global optimization methods

  • One-way and two-way models

  • Deep Bidirectional Transformers for Language Understanding ​

  • Exercise 21: Deep Learning LSTM

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Module 14: Generative Adversarial Networks (GANs)

 

  • Generative Adversarial Networks (GANs)

  • Fundamental components of the GANs

  • GAN architectures

  • Bidirectional GAN

  • Training generative models

  • Exercise 22: Deep Learning GANs

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Module 15: Tuning Hyperparameters

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  • Hyperparameterization

  • Grid search

  • Random search

  • Bayesian Optimization

  • Train test split ratio

  • Learning rate in optimization algorithms (e.g. gradient descent)

  • Selection of optimization algorithm (e.g., gradient descent, stochastic gradient descent, or Adam optimizer)

  • Activation function selection in a (nn) layer neural network (e.g. Sigmoid, ReLU, Tanh)

  • Selection of loss, cost and custom function

  • Number of hidden layers in an NN

  • Number of activation units in each layer

  • The drop-out rate in nn (dropout probability)

  • Number of iterations (epochs) in training a nn

  • Number of clusters in a clustering task

  • Kernel or filter size in convolutional layers

  • Pooling size

  • Batch size

  • Exercise 23: Optimization Xboosting, Random forest and SVM

  • Exercise 24: Optimized Deep Learning

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Probabilistic Machine Learning 

 

Module 16: Probabilistic Machine Learning

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  • Introduction to probabilistic machine learning

  • Gaussian models

  • Bayesian Statistics

  • Bayesian logistic regression

  • Kernel family

  • Gaussian processes

    • Gaussian processes for regression

  • Hidden Markov Model

  • Markov chain Monte Carlo (MCMC)

    • Metropolis Hastings algorithm

  • Machine Learning Probabilistic Model

  • Bayesian Boosting

  • Bayesian Neural Networks

  • Exercise 25: Gaussian process for regression

  • Exercise 26: Bayesian Neural Networks

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Model Validation

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Module 17: Validation of traditional and Machine Learning models

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  • Model validation

  • Validation of machine learning models

  • Regulatory validation of machine learning models in Europe

  • Out of Sample and Out of time validation

  • Checking p-values in regressions

  • R squared, MSE, MAD

  • Waste diagnosis

  • Goodness of Fit Test

  • Multicollinearity

  • Binary case confusion matrix

  • K-Fold Cross Validation

  • Diagnostico del modelo

  • Exercise 27: Validación avanzada de la regression

  • Exercise 28: Diagnostico de la regresión

  • Exercise 29: K-Fold Cross Validation in R

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Module 18: Advanced Validation of AI Models

 

  • Integration of state-of-the-art methods in interpretable machine learning and model diagnosis.

  • Data Pipeline

  • Feature Selection

  • Black-box Models

  • Post-hoc Explainability

  • Global Explainability

  • Local Explainability

  • Model Interpretability

  • Diagnosis: Accuracy, WeakSpot, Overfit, Reliability, Robustness, Resilience, Fairness

  • Model comparison

    • Comparative for Regression and Classification

    • Fairness Comparison

  • Exercise 30: Validation and diagnosis of advanced credit scoring models

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Auto Machine Learning and XAI

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Module 19: Automation of ML

 

  • What is modeling automation?

  • That is automated

  • Automation of machine learning processes

  • Optimizers and Evaluators

  • Modeling Automation Workflow Components​

  • Hyperparameter optimization

  • Global evaluation of modeling automation

  • Implementation of modeling automation in banking

  • Technological requirements

  • Available tools

  • Benefits and possible ROI estimation

  • Main Issues

  • Genetic algorithms

  • Exercise 31: Automation of the modeling, optimization and validation of pricing models

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Explainable Artificial Intelligence

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Module 20: Explainable Artificial Intelligence XAI

 

  • Interpretability problem

  • Machine learning models

    • 1. The challenge of interpreting the results,

    • 2. The challenge of ensuring that management functions adequately understand the models, and

    • 3. The challenge of justifying the results to supervisors

  • ​Black Box Models vs. Transparent and Interpretable Algorithms

  • Interpretability tools

  • Shap, Shapley Additive explanations

    • Global Explanations

    • Dependency Plot

    • Decision Plot

    • Local Explanations Waterfall Plot

  • Lime, agnostic explanations of the local interpretable model

  • Explainer Dashboard

  • Other advanced tools

  • Exercise 32: XAI interpretability of pricing

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Quantum Computing

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Module 21: Quantum computing and algorithms

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Objective: Quantum computing applies quantum mechanical phenomena. On a small scale, physical matter exhibits properties of both particles and waves, and quantum computing takes advantage of this behavior using specialized hardware. The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basic" states, meaning that it is in both states simultaneously.

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  • Future of quantum computing in insurance

  • Is it necessary to know quantum mechanics?

  • QIS Hardware and Apps

  • quantum operations

  • Qubit representation

  • Measurement

  • Overlap

  • Matrix multiplication

  • Qubit operations

  • Multiple Quantum Circuits

  • Entanglement

  • Deutsch Algorithm

  • Quantum Fourier transform and search algorithms

  • Hybrid quantum-classical algorithms

  • Quantum annealing, simulation and optimization of algorithms

  • Quantum machine learning algorithms

  • Exercise 33: Quantum operations multi-exercises

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Module 22: Introduction to quantum mechanics

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  • Quantum mechanical theory

  • Wave function

  • Schrodinger's equation

  • Statistical interpretation

  • Probability

  • Standardization

  • Impulse

  • The uncertainty principle

  • Mathematical Tools of Quantum Mechanics

  • Hilbert space and wave functions

  • The linear vector space

  • Hilbert's space

  • Dimension and bases of a Vector Space

  • Integrable square functions: wave functions

  • Dirac notation

  • Operators

  • General definitions

  • Hermitian adjunct

  • Projection operators

  • Commutator algebra

  • Uncertainty relationship between two operators

  • Operator Functions

  • Inverse and Unitary Operators

  • Eigenvalues and Eigenvectors of an operator

  • Infinitesimal and finite unit transformations

  • Matrices and Wave Mechanics

  • Matrix mechanics

  • Wave Mechanics

  • Exercise 34: Quantum mechanics multi-exercises

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Module 23: Introduction to quantum error correction

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  • Error correction

  • From reversible classical error correction to simple quantum error correction

  • The quantum error correction criterion

  • The distance of a quantum error correction code

  • Content of the quantum error correction criterion and the quantum Hamming bound criterion

  • Digitization of quantum noise

  • Classic linear codes

  • Calderbank, Shor and Steane codes

  • Stabilizer Quantum Error Correction Codes

  • Exercise 35: Noise Model, Repetition Code and quantum circuit

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​Module 24: Quantum Computing II

 

  • Quantum programming

  • Solution Providers

    • IBM Quantum Qiskit

    • Amazon Braket

    • PennyLane

    • cirq

    • Quantum Development Kit (QDK)

    • Quantum clouds

    • Microsoft Quantum

    • Qiskit

  • Main Algorithms

    • Grover's algorithm

    • Deutsch–Jozsa algorithm

    • Fourier transform algorithm

    • Shor's algorithm

  • Quantum annealers

  • D-Wave implementation

  • Qiskit Implementation

  • Exercise 36: Quantum Circuits, Grover Algorithm Simulation, Fourier Transform and Shor​

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Module 25: Quantum Machine Learning

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  • Quantum Machine Learning

  • Hybrid models

  • Quantum Principal Component Analysis

  • Q means vs. K means

  • Variational Quantum Classifiers

  • Variational quantum classifiers

  • Quantum Neural Network

    • Quantum Convolutional Neural Network

    • Quantum Long Short Memory LSTM

  • Quantum Support Vector Machine (QSVC)

  • Exercise 37: Quantum Support Vector Machine​

 

​Module 26: Tensor Networks for Machine Learning

 

  • What are tensor networks?

  • Quantum Entanglement

  • Tensor networks in machine learning

  • Tensor networks in unsupervised models

  • Tensor networks in SVM

  • Tensor networks in NN

  • NN tensioning

  • Application of tensor networks in credit scoring models

  • Exercise 38: Neural Network using tensor networks

 

Healthcare Models

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​Module 27: Early Obesity Detection Using AI and Quantum AI

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Obesity is an epidemic disease, as being overweight or obese increases the risk of serious diseases such as diabetes, heart disease, hypertension and certain types of cancer that lead to premature death. However, early identification of the causative factors makes obesity highly preventable. Due to the objective of early detection and with the advancement of machine learning (ML) algorithms, some models used in obesity detection are evaluated against quantum ML algorithms.

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  • Obesity in the world

  • What are the causes of obesity and overweight?

  • What are the health consequences of overweight and obesity?

  • How to reduce overweight and obesity?

  • Variables related to eating habits

  • Variables related to physical condition

  • Feature Engineering

  • Importance in variable selection

  • Treatment of outliers

  • Models used in recent years to detect obesity

  • Machine Learning Models

    • Support Vector Machine Regression

    • K-Nearest Neighborhood

    • Random Forests

    • Gradient Boosting

    • Extreme Gradient Boosting

  • Quantum Machine Algorithms

    • Qubit and Quantum States

    • Quantum circuits

    • Support Vector Quantum Machine

    • Variational quantum classifier

    • Quantum Neural Networks

  • Exercise 39: Random Forest, Gradient Boosting and Extreme Gradient Boosting to detect obesity

  • Exercise 40: Quantum Support Vector Machine  and classical SVM to detect obesity

  • Exercise 41: Quantum Neural Networks to detect obesity

  • Exercise 42: Quantum Convoluted Neural Networks to detect obesity

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Module 28: Diabetic Retinopathy Model with Convolutional Neural Networks

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Diabetic retinopathy is one of the main causes of blindness in diabetic people between 25 and 65 years old. Injuries to the retina caused by weakened blood vessels can lead to vision loss and even total blindness. Current manual grading methods to detect diabetic retinopathy are time-consuming and error-prone. Convolutional neural networks have shown great promise for automating the identification and categorization of diabetic retinopathy.​

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  • Excessive blood sugar levels

  • Statistics in the world on diabetes

  • Diabetes-related retinopathy (DR)

  • Four phases

    • ​Mild non-proliferative DR (NPRD)

    • Moderate non-proliferative DR (NPRD)

    • Severe NPDR

    • Proliferative DR (PDR)

  • Image preprocessing and diagnosis process of diabetic retinopathy using CNN

  • Classification of images in Real Estate

    • ​Problem Statement

    • Deep Learning Problem Formulation

    • Project and Data Source

    • Image Dataset

    • Evaluation Metric

    • Exploratory Data Analysis

    • Image Preprocessing

    • Model Training

    • Productionizing

  • Image Classification: Data-driven Approach

  • Convolutional Neural Networks

  • Data and the Loss preprocessing

  • Hyperparameters

  • Train/val/test splits

  • L1/L2 distances

  • Hyperparameter search

    • Optimization: Stochastic Gradient Descent

    • Optimization landscapes

    • Black Box Landscapes

    • Local search

    • Learning rate

  • Weight initialization

  • Batch normalization

  • Regularization (L2/dropout)

  • Loss functions

  • Gradient checks

  • Sanity checks

  • Hyperparameter optimization

  • Architectures

    • Convolution / Pooling Layers

    • Spatial arrangement

    • Layer patterns

    • Layer sizing patterns

    • AlexNet/ZFNet/Densenet/VGGNet

  • Convolutional Neural Networks tSNE embeddings

  • Deconvnets

  • Data gradients

  • Fooling ConvNets  

  • Human comparisons Transfer Learning and

  • Fine-tuning Convolutional Neural Networks

  • Performance Metrics

    • Accuracy

    • F1-Score

    • AUC-ROC

    • Cohen Kappa Coefficient

  • Exercise 43: Convolutional Neural Networks for diagnosis of diabetic retinopathy

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Module 29: Drug discovery using

GAN neural networks

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Drug discovery refers to the process of identifying and developing new chemical compounds, to create medications that can treat or cure diseases. In this process, one of the biggest obstacles is designing a molecule with the necessary properties, since it requires numerous chemical and structural optimizations. Generative adversarial networks can learn the representation of chemical structures and drug properties from the data set to produce new chemical structures that have similar properties to the training set. Generative adversarial networks have the potential to accelerate drug discovery by generating new compounds with desirable properties, thereby reducing the time and cost required for traditional drug discovery methods.

 

  • Simplified Molecular Input Line Entry System (SMILES)

  • Drug Discovery Cycle

  • Generative Adversarial Networks (GANs)

  • Backpropagation in Discriminator and Generator Training

  • Encoding

  • GAN Variants

    • CGAN

    • LAPGAN

    • DCGAN

    • AAE

    • INFOGAN

  • Application of the GAN

    • Drug discovery

    • Drug development

    • Biomolecular

    • Targeting

  • ​Modified and Applied CNN architectures used as the GAN generators and discriminators

  • DCGAN generator and discriminator used for molecular compound fingerprint generation

  • Exercise 44:  Drug discovery models using GAN neural networks

 

Quantum Computing for Healthcare

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Module 30: Applications of quantum computing in clinical and medical solutions

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Medicine, including health and life sciences, has witnessed a flurry of activities and experiments related to quantum computing in recent years. Initially they focused on biochemical and computational biology problems, but recently clinical and medical quantum solutions have attracted increasing interest. The rapid emergence of quantum computing in the fields of health and medicine requires an adaptation of the landscape.

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  • Impact of quantum computing in healthcare

  • Quantum support vector classifier (QSVC)

    • Virtual screening in drug discovery

  • QNN, QSCV

    • Classification of ischemic heart disease

  • Transfer learning-based QNN

    • Classification of breast cancer​​

  • VQC

    • Classification of diabetes

  • QSVC

    • Classification of medication persistence for individuals with rheumatoid arthritis

  • Grover’s

    • ​DNA sequence alignment

  • Medical quantum computing challenges

    • Data Security

    • Replicability

    • Skill devolpment

  • Exercise 45: Hybrid Quantum Classical Neural Networks for Diabetic Retinopathy

  • Exercise 46: Experimentos de diabetes usando Neural Network clásico y Quantum Neural Network 

  • Exercise 47: Esophageal cancer tissue sorter using Neural Network clásico y Quantum Neural Network 

  • Exercise 48: Quantum GAN for drug discovery

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Probabilistic Machine Learning for Healthcare

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Module 31: Bayesian Neural Network

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 DL models have also been intensively used in different healthcare tasks, such as disease diagnosis and treatment. ML techniques have outperformed other machine learning algorithms and have proven to be the ultimate tools for many next-generation applications. Despite all that success, classical deep learning has limitations and its models tend to be overconfident in their predicted decisions because they don't know when they're wrong. For healthcare, this limitation can have a negative impact on model predictions, since almost all decisions regarding patients and diseases are sensitive. Therefore, Bayesian deep learning (BDL) has been developed to overcome these limitations. Unlike classical DL, BDL uses probability distributions for the model parameters, allowing all uncertainties associated with the predicted results to be estimated. In this sense, the BDL offers a rigorous framework to quantify all sources of model uncertainty.

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  • Convolutional Neural Networks (CNN)

  • Markov chain Monte Carlo (MCMC)

    • Metropolis Hastings algorithm

  • Monte Carlo Dropout (MC–DROPOUT)

  • Variational Inference (VI)

  • Bayesian Neural Networks

  • Exercise 49: Bayesian Neural Networks for Diabetic Retinopathy Detection 

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