Softmax

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Introduction The softmax function is essential in mathematics and machine learning. It transforms a vector of real numbers into a probability distribution. Put simply, it converts a set of numbers into probabilities. To understand how it works, let’s look at a list of risk values for some patients: Patients Surgical Risk Patient A 2 Patient … Read more

Orientation in Dicom

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Understanding DICOM Coordinate Systems and Image Orientation: Why Your 3D Volume Looks Upside Down 1. Introduction — Why Orientation Matters Have you ever opened a medical image and found the anatomy upside down or mirrored? It’s not your viewer’s fault — it’s about geometry. DICOM files contain not only pixels, but also the mathematical information … Read more

TCP and UDP protocol Benchmarking with Python: From Theory to Practice with FHIR APIs in Healthcare

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A technical comparison between TCP and UDP protocols implemented in Python: examining performance metrics, security considerations, and practical applications within healthcare systems using FHIR standards for effective data exchange between medical platforms. Introduction: The Significance of TCP vs UDP When browsing websites, streaming videos, or making video calls, our data travels across networks using protocols … Read more

Create a Medical Database with Docker: Complete Guide with SQLAlchemy, and Flask

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Introduction: Why Build a Medical Database with Docker? Creating a robust medical database system requires careful consideration of security, scalability, and maintainability. Furthermore, Docker containerization offers an ideal solution for healthcare applications by providing isolated environments that ensure consistent deployment across different systems. In this comprehensive tutorial, we’ll explore how to create a medical database … Read more

Sensitivity Analysis

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Definition Sensitivity analysis is a collection of techniques that determine how input parameters affect model results. Specifically, it measures how much variation in the results stems from different types of uncertainty. For a model: $Y=f(X_1,X_2,X_3…..X_n)$ examines how Y changes when each X is modified. Sensitivity analysis can be applied across several key areas: predictive models, … Read more

Random Numbers in Python

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Why do we need random number generation in statistics and data science? Data scientists and statisticians rely on random number generation for several important purposes. They can be used to create data samples, which serves as a foundation for advanced statistical techniques. This includes Bootstrapping methods that involve resampling from existing data to create new … Read more

Programming Paradigms in Python

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A programming paradigm is the model or approach used to logically organize a program. It defines how different parts of a program interact and work together. A programming paradigm encompasses three key aspects: Paradigms Let’s explore the main types of programming paradigms Imperative Paradigm This paradigm involves giving the computer explicit, step-by-step instructions using variables, … Read more

Visualizing Statistical Distributions with Python

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This post illustrates techniques for visualizing statistical distributions using Python and its graphics libraries, particularly Matplotlib. The resulting charts are used in the statistical distributions lesson of the statistics course. Required Libraries Import Normal distribution Exponential distribution Bernoulli distribution Binomial distribution Poisson distribution Uniform distribution Chi square distribution t distribution vs normal distribution Sigmoid function

Inside Dicom

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In a previous blog post, we explored how to read the content of a DICOM file, including its numerous tags. These tags provide insights into the study type, characteristics, and all relevant patient and study information. Now, we’ll focus on the most crucial tag—the one containing the images. A typical study can include anywhere from … Read more