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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

A Gentle Introduction to AI for Medical Imaging

Shruti
Shruti Last Updated : 23 Feb, 2021
5 min read

This article was published as a part of the Data Science Blogathon.

Introduction

Medical Imaging plays an important role in modern medicine. It allows us to visualize and examine the human body in depth which manifests structures inside our body in great detail. Nowadays, medical devices are constantly becoming more advanced in terms of — cost, precision, and safety — to improve the medical experience of the end-users.

After the recent boost in the field of AI, effective solutions came to light to solve countless intricate problems in multiple sectors using both machine learning and deep learning algorithms.

One such area that has gained continuous momentum in the aspect of research and development is the healthcare domain.

Few reasons why AI in healthcare is flourishing day by day are as follows:-

  1. The amount of data generated from medical devices is extensive- In an era where data is produced faster than its consumed, processing such a volume of data by humans alone induces a lot of physician burnout.
  2. Furnish high accuracy and precision- AI can enhance efficiency, reduce human errors, and achieve goals with the minimal human operation.
  3. Extremely cost-effective- developing and maintaining AI solutions are less expensive when compared to bringing in doctors from across the globe to screen a patient’s health conditions.

 

The flow of AI in medical imaging

Medical imaging AI

Fig:- Real-world flow of medical imaging- 1) Imaging tools, 2) radiologist to provide insights about images, 3) Picture archiving and communication system(PACS) to store image and patient data, 4) Diagnostic Clinician to examine patients and render a diagnosis. AI can be incorporated in any of the steps in the flow.

Now let us dive into each segment present in the flow in order to understand the process better. Firstly, imaging tools are used to generate image data using advanced imaging techniques.

Imaging tools

Diagnostic imaging tools help narrow the causes of an injury or illness and ensure that the diagnosis is accurate. These techniques include X-Rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI).

These imaging tools let your doctor “see” inside your body to get a “picture” of your bones, organs, muscles, tendons, nerves, and cartilage. Such that the doctor can determine if there are any abnormalities present.

Imaging tools are broadly classified into two categories: 2-Dimensional and 3-Dimensional imaging tools.

  • 2D imaging: an imaging technique where pictures are taken from a single angle. Example:- X-Ray, Ultra Sound Scan, and Microscopy.
  • 3D imaging: an imaging technique where pictures are taken from different angles to create a volume of images. Example:- Computed Tomography (CT), Magnetic Resonance Imaging (MRI)

The three most commonly used imaging tools include x-rays, computed tomography (CT) scans, and magnetic resonance imaging (MRI) which are differentiated in the table below.

Different Clinical Tools AI Medical imaging

The generated data from the imaging tools are viewed by Radiologists, who are the primary readers of the image. They analyze the image and write a small note based on their observations, which is understood by a commoner without any medical knowledge. Radiologists create a specific file format called DICOM combining all the data present upon which the data is moved to a storage unit called PACS using specific file formats.

Picture Archiving and Communication System(PACS)

PACS Systems are a medical imaging technology that provides economical storage, retrieval, management, distribution, and presentation of medical images. Electronic images and reports are transmitted digitally via PACS systems. This eliminates the need to manually file, retrieve, or transport film jackets. It allows a healthcare organization (such as a hospital) to capture, store, view, and share all types of images both internally and externally.

Picture Archiving and Communication System(PACS)

Source: Wikipedia

        

Equivalent to pdfs and docs on our system folder, PACS stores files either in DICOM or NIFITI format. Digital Imaging and Communications in Medicine(DICOM) is the standard for the communication and management of medical imaging information and related data. DICOM files consist of both patient information along with image information and modality.

The final step in the flow is a Diagnostic Clinician who takes the report provided by the radiologist from the storage system along with a few other parameters like, patient’s current condition/symptoms, other labs result, and prior medical knowledge to provide the diagnosis.

Different Machine Learning Techniques Used

1. Classification:– Classification is a technique where we categorize data into a given number of classes. The main goal of a classification problem here is to identify whether a disease or anomaly is present in the image.

Use case- Identifying the presence of Pneumonia from lung X-Rays.

Image for post

 

2.Segmentation:- Segmentation in Image Processing is being used in the medical industry for efficient and faster diagnosis, detecting diseases by identifying specific pixels in an image that contains a structure or finding.

Use case- Identify tumors and calculate the size of the abnormality.

Image for post

Source: Chimaera GmbH gallery

 

 3. Localization:- Localization is used to narrow down the area of the image that may contain the abnormality.

Use case- Helps to draw attention to mass in breasts to identify the presence of Breast cancer.

The final step after building a machine learning model is to get medical approval from a certified regulatory body such as — FDA. FDA is a federal agency that is responsible for protecting public health by ensuring the safety, efficacy, and security of human and biological products. Only upon approval from the regulatory body allows us to use the model/solution to treat patients in the real-world. Which generally is a very time taking process with a lot of standards that need to be met.

AI in the healthcare industry can be used in various scenarios from disease detection to prioritization of records in PACS to fasten the retrieval process. However, any solution that is provided using algorithms irrespective of its high performance can only contribute to the betterment of the results but not to replace Radiologists/Doctors as the scope of the model is limited to a specific problem statement and will fail to take into account a holistic view of a patient’s condition.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

Shruti
Shruti

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