Exploring BreastMNIST: AI Tools, Binary Classification & Data Visualization Guide

We dive deep into BreastMNIST—a publicly available binary classification dataset designed for breast ultrasound imaging—and discuss why it's an ideal starting point for anyone looking to understand medical image processing, model training, and AI integration. I also reflect on the surprising performance insights related to image resolution, and why more pixels don’t always lead to better results.

You'll also get a behind-the-scenes look at how I'm developing these tools live, using simple tech stacks like vanilla JavaScript, HTML, CSS, and JSON. Plus, we talk about future plans to turn blogs into full-fledged interactive applications, explore the role of AI agents, and how everything ties back to open science and creative commons principles.

🔗 Dive into the tools and dataset here:

Whether you're into biomedical research, machine learning, or just curious about the potential of interactive AI-powered blogs—this video offers a practical and conceptual guide to what’s possible.

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#BreastMNIST
#MedicalAI
#DeepLearning
#AIInHealthcare
#BiomedicalTools
#DataVisualization
#MachineLearning
#OpenScience
#MedicalImaging
#BionicChaos

0:00 Introduction to Bionic Chaos
0:07 Exploring the website's homepage
0:22 Scrolling through available interactive tools
0:51 Deep dive into the Cochlear Simulator
1:12 More tools: Eye Tracker, EEG Music, Cardiobot
1:41 Suggestions for future tools and improvements
2:33 Adding tutorials and keeping up with tech changes
3:01 Overview of the site’s purpose and blog plans
3:13 Advanced tools for ECG and EEG analysis
3:46 Live development discussion and viewer engagement
4:10 Testing Google AI Studio for model training
4:29 Reviewing dataset structure and training results
5:09 Image resolution performance anomaly
6:08 Dataset access and Creative Commons use
6:18 Blog plans and dataset comparisons
6:36 Why BreastMNIST is the dataset of choice
6:59 Binary vs multi-class classification explanation
7:08 Introduction to processing workflow
7:23 Data download and parsing
7:40 Visualization and reviewing image structure
8:00 Incorporating AI agents for automation
8:20 Results analysis and training metrics
8:33 Creating interactive blog with results display
9:00 Heatmaps, accuracy plots, and visual insights
10:00 Prompt ideas for blog generation
11:04 Resizing issues and dataset integrity
12:00 Public domain access and licensing variations
13:04 Intro to BreastMNIST and its advantages
13:59 Performance comparison across architectures
15:14 Model choice and metric interpretation
15:38 Walkthrough: dataset download and visualization
16:26 Training CNNs and reviewing output
17:06 Resolution vs performance dilemma
17:54 Workflow efficiency and data simplicity
18:07 Using AI agents to streamline training
18:43 HTML/CSS/JS tools for real-time metrics
19:01 Conclusion: Why BreastMNIST is ideal for beginners
19:24 Combining AI, open access, and educational tools
20:01 Enhancing interactivity in medical data blogs
20:56 Emphasizing interpretability and insight
22:00 Summary of key points and future improvements
23:10 Final thoughts and engagement invitation