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Reading list

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

This is How Bakery’s AI Made Cancer Detection a Piece of Cake

Nitika Sharma
Nitika Sharma Last Updated : 08 Apr, 2024
3 min read

AI is dynamically impacting the healthcare sector, and cancer detection is no exception. From diagnosing early-stage breast cancer to pinpointing the origin of unknown tumors, AI is solving several complex healthcare problems.

But have you ever heard of a bakery playing a role in this fight?

This article explores the incredible journey of BakeryScan, a seemingly ordinary bakery AI system developed by Hisashi Kambe’s team at BRAIN Co., Ltd. in Japan. BakeryScan’s initial purpose was far removed from the medical field, but it took an unexpected turn and became a potential game-changer in cancer cell detection.

What was the Need of Bakery AI?

Japanese bakeries needed AI for their checkout systems because of two key challenges:

  1. Huge Variety of Pastries: Unlike Western bakeries with a limited selection, Japanese bakeries offer hundreds of different and constantly evolving pastries. Traditional barcode scanners wouldn’t work for this vast and ever-changing range.
  2. Unwrapped Pastries: For aesthetic reasons, Japanese bakeries often display pastries unwrapped. This eliminates the option of pre-assigning barcodes, making manual identification by cashiers slow and inefficient.

AI, specifically BakeryScan, solved these problems by using cameras and deep learning to visually identify pastries based on their features (shape, color, texture) without needing barcodes. This sped up checkouts and improved customer experience.

Next Few Years of BakeryScan..

BakeryScan’s success hinges on its sophisticated AI technology. This AI can not only distinguish between similar breads but also adapt and learn over time. When unsure about an item, BakeryScan suggests possibilities to the operator, incorporating their choice to improve future accuracy. This “learning like a human” approach has propelled BakeryScan’s identification rates to a staggering 97% and beyond.

BRAIN’s innovation extends far past bread. The AI powering BakeryScan, dubbed AI-Scan, is being used in Japan to identify mixed prescription medications. Looking ahead, BRAIN aims to expand globally, potentially licensing AI-Scan to supermarkets, cafeterias, and POS system manufacturers in the US and Europe.

Bakery Ai

Bakery AI to Cancer Cell Detection

In 2017, a Doctor working for Kyoto’s Louis Pasteur Center for Medical Research saw an ad for BakeryScan. After some thinking he came to a quite funny realization that BakeryScan could possibly detect cancer cells. This sparked a collaboration between the doctor and BRAIN Co., the company behind BakeryScan.

The initial tests proved remarkably successful. BRAIN’s AI, adapted for medical applications, demonstrated a 98% accuracy rate in identifying cancerous cells. Further refinements and optimizations for analyzing large numbers of cells led to the creation of Cyto-AiSCAN.

Currently undergoing evaluation in major hospitals in Kyoto and Kobe, Cyto-AiSCAN has the potential to revolutionize cancer detection. When questioned about the underlying technology used in Cyto-AiSCAN, BRAIN Co.’s CEO, Hisashi Kambe, offered a cryptic yet intriguing response. “Original way,” he stated, followed by a smile and the enigmatic remark, “Same as bread.” This suggests that Cyto-AiSCAN might not rely solely on traditional deep learning methods, sparking curiosity about the innovative approach BRAIN Co. has developed.

Bakery AI to Cancer Cell Detection

Beyond the Bakery

The story of BrainCo’s AI is a shining example of how innovation often occurs in the most unexpected places. It underscores the importance of cross-disciplinary thinking and the unforeseen benefits that can arise from solving mundane problems. Today, this AI is heralding a new era in medical diagnostics, offering hope for early and more accurate cancer detection.

Checkout the role of Generative AI in Healthcare.

Summing-up

When Brain Co. made BakeryScan in 2013 to help sort pastries, they didn’t know it could become something so important. BakeryScan started small but grew into a tool that might help diagnose cancer in the future. This shows that even little ideas can lead to big changes. Science can be hard to understand, but if we keep exploring, we can figure it out.

Like BakeryScan, we can also keep trying until we reach our ultimate goal.

Let’s keep inventing for a better tomorrow.

Click here to explore more such amazing content around AI tools.

Application of AIBakery AiCancer DetectionComputer Vision
Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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