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Case Study

Cancer Cell Consensus Map

We’ve used computer vision and unsupervised learning algorithm to provide better assistance to doctors and healthcare providers when analyzing Microscopic Images. Technological innovation is making it possible to provide better assistance to doctors and healthcare providers in the diagnosis and treatment of various diseases.

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Overview

We developed a clustering algorithm to create a consensus map for hand-labeled microscopic images targeting cancer cells, improving diagnosis and providing training data for ML models. Our approach addressed business problems related to modeling complexity, data size, and scalability. The successful POC provided the customer with accurate labeled data for advanced ML supervised models.

Challenge

Our training base consisted of heterogeneous Microscopic Images, with cancer cells hand labeled by doctors.

Problem

Approach

Our analytical algorithm for cell labeling consensus used an unsupervised machine learning approach (HDBSCAN) to automatically find optimal clusters. The resulting labels where augmented with data from domain knowledge and hierarchical rules on annotations to create a consensus map.

Clustering Algorithm

We’ve tested multiple clustering approaches such as distance-based, hierarchical or density based clustering. These clustering approaches were evaluated based on their ability to accurately identify groups or clusters within the data, as well as their scalability and computational efficiency. We’ve ended up using DBSCAN as it striked the right balance between targeting the correct cells and removing outliers.

Machine Learning Model

Time Line

01

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Gather Images with Labels

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Experiment with Different Clustering Methods

03

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Build Baseline Consensus Map

04

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Add Heuristic Layer

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Key Insights

Our algorithm facilitated medical professionals to achieve greater accuracy and consensus on cell labeling through a shared map, potentially leading to improved targeting of cancer cells.

The use of unsupervised machine learning methodologies allowed for the development of an algorithm that did not rely on annotated/ground truth data for every new data problem, making it a more widely applicable solution in the field of cell labeling.

The consensus map can now be used for other machine learning algorithms as a labeled dataset.