Nuclei Detection With Histopathological Images Through Machine Learning
Afya Shaikh
Lay Summary:
Cancerous skin tissue poses a severe threat to the body and traditional methods to detect these cancerous cells can be slow and error-prone, which can delay diagnosis and treatment. This summer I developed a machine-learning model that can rapidly and accurately identify cancerous nuclei in histopathological images, which demonstrates how advanced algorithms can be effectively integrated into healthcare.
Abstract:
Early and accurate cancer detection is vital for effective treatment and improved patient outcomes. In particular, skin cancers can be highly aggressive and spread quickly, making swift diagnoses crucial. Traditional histopathological methods for identifying cancerous cells can be time-consuming and prone to errors, adversely affecting patient care. This research aims to enhance the efficiency and accuracy of cancer detection through a machine-learning model that uses histopathological images. Skin cancers often involve abnormal cell nuclei that appear larger and darker due to excessive DNA. Detecting these changes early is challenging, as cancer cells may develop deep within tissues and standard diagnostic methods can be slow and error-prone. I created a machine-learning model to automate the detection of cancerous cells in histopathological images. The dataset includes 30 digitized Hematoxylin and Eosin (H&E)-stained frozen sections from ten different human organs, sourced from The Cancer Genome Atlas (TCGA). These sections cover a variety of tissue types and staining conditions, providing a comprehensive basis for training and validating the algorithm. The dataset includes over 8,000 annotated nuclei and various segmentation masks to support the development of the model. The machine learning model demonstrated significant improvements in detecting cancerous nuclei compared to traditional methods. By leveraging advanced segmentation techniques, the model increased the accuracy and speed of identifying cancerous cells in histopathological images. Automating nuclei detection with machine learning reduces the time required for analysis and minimizes the risk of errors in identifying these cancerous cells. This can lead to more precise surgical planning and better patient outcomes by facilitating early detection and accurate diagnosis. Applying machine learning to nuclei detection in histopathological images represents a promising advancement in cancer diagnostics. The improved efficiency and accuracy provided by this model can significantly enhance the detection and treatment planning for skin cancers, ultimately benefiting patient care and outcomes.
Q&A:
Bios: Afya Shaikh
Program Track: Skills Development
GitHub Username:
AfyaS -Afya Shaikh
What was your favorite seminar? Why?
My favorite seminar was definitely Louis Vaickus’s series. His introductions to pathology and machine learning were so insightful and inspiring, giving me a great sense of what to expect this summer. I felt excited and prepared for what was ahead thanks to his engaging presentations. -Afya Shaikh
If you were to summarize your summer internship experience in one sentence, what would it be?
This internship was a supportive and inspiring experience, as it allowed me to create a project I was passionate about while receiving guidance and motivation, and learning many new topics and ideas throughout it all. -Afya Shaikh
Blog Post
Is it possible to create a machine-learning model that can detect
cancerous nuclei in tissue images?
By: Afya Shaikh - EDIT ML 2024
Understanding the Problem
Cancerous skin tissue is a serious concern, characterized by cells that grow
uncontrollably and can invade other parts of the body. This uncontrolled growth disrupts normal skin functions and can lead to severe health issues if not detected early.Traditional methods of detecting these cancerous cells—like light microscopy and histopathology—are great methods but they come with notable limitations. These methods can be slow, prone to errors, and might delay diagnosis and treatment, allowing the cancer to progress unchecked.
Why I Took on This Challenge
Recognizing the urgency of improving cancer detection, I decided to develop this project this summer with a clear goal: to develop a faster, more accurate method for spotting cancerous nuclei in histopathological images. Traditional methods, while valuable, have their flaws, and I aimed to address these by using the power of machine learning.
The Solution: Machine Learning Magic
To tackle the problem, I created a machine-learning model designed to identify cancerous nuclei in tissue images. Unlike traditional methods, this model leverages the power of algorithms to analyze large amounts of data quickly and with high precision.
Here’s how I approached it:
1. Data Collection and Preparation: I used a dataset from Kaggle, featuring H&E-stained tissue samples from The Cancer Genome Atlas (TCGA). This dataset included over 8,000 annotated nuclei and segmentation masks. I standardized these images to a uniform size and normalized the pixel values to ensure consistent and accurate training.
2. Training the Model: I built and trained the model using Jupyter Notebook, employing a U-Net architecture with a ResNet18 encoder. This setup is renowned for its effectiveness in image segmentation tasks. The model was rigorously evaluated using precision, recall, and accuracy metrics to ensure reliable performance.
3. Enhancements: To improve the model’s learning, I divided each image into smaller patches, allowing the model to learn from various regions and reducing
bias. I also split the dataset into training and validation sets to ensure robust evaluation.
What I Discovered
The results were promising! The model achieved an impressive accuracy rate of around 95% in detecting cancerous nuclei. Moreover, its processing speed was notably efficient, allowing for rapid analysis of images. These results suggest that machine learning could significantly enhance cancer detection by providing quicker and more accurate diagnoses.
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Figure 1. The machine learning models precision, recall, F1-score, and accuracy of nuclei detection.
Learning Curves
Taking on this project was both a personal and technical challenge. While navigating through the complexities and occasional frustrations, the journey proved incredibly rewarding. One of the significant hurdles was dealing with limitations in the dataset. Ideally, a larger and more diverse dataset would enable the creation of an even more advanced and accurate model.
I also faced difficulties in ensuring that my model could effectively interpret the images.
Initially, resizing the images led to substantial losses, impacting the model’s performance. However, these learning curves are an inherent part of the process.
Figure 2. The machine learning models low-accuracy images of nuclei detection.
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Through trial and error, I discovered that dividing the images into smaller quarters significantly improved the model’s ability to learn from various regions of the tissue. This adjustment was a pivotal moment, highlighting the importance of perseverance and adaptability in overcoming technical challenges.
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Figure 3. The machine learning models high-accuracy images of nuclei detection.
What’s Next?
Despite the success, there’s still work to be done. Future steps include expanding the dataset to cover more diverse tissue types, optimizing the model for even faster performance, and fine-tuning its parameters for improved accuracy. Additionally, comparing this model with others in the field will help refine and enhance its
effectiveness.