Graph Neural Networks for Immune Cell Prediction Accuracy
Sohan Arigela & Srihas Motati
Lay Summary:
We are developing an advanced computer model to automatically find and map immune cells, like white blood cells, in tissue samples, which could help doctors better understand and treat diseases like cancer. By using cutting-edge technology, our approach aims to make the analysis of these cells faster, more accurate, and easier to perform.
Abstract:
Understanding the spatial organization and interactions of immune cells is critical for advancing research in areas like autoimmune disorders and cancer immunotherapy. Traditional methods of analyzing immune cells are often difficult to detect. To address this issue, our project uses Graph Neural Networks (GNNs) to detect immune cells in tissue samples. We utilize annotated whole slide images (WSIs), to accurately locate white blood cells within complex cell clusters. Our approach uses the contextual power of GNNs to identify these cells, offering a scalable and objective solution for immune cell analysis. The performance of our model is evaluated using metrics such as accuracy and F1-score, with the goal of being able to detect immune cells in tissue samples.
Q&A:
Bios: Sohan Arigela,Srihas Motati
Program Track: Skills Development
GitHub Username:
SohanAI -Sohan Arigela
srihasmotati -Srihas Motati
What was your favorite seminar? Why?
my favorite seminar was the very first one since it gave us a very solid introduction into the program and set a tone for the rest of our internship -Sohan Arigela
I enjoyed the first seminar because it showed me what I am getting into. -Srihas Motati
If you were to summarize your summer internship experience in one sentence, what would it be?
A summer full of researching machine learning algorithms to address medical shortcomings -Sohan Arigela
My summer internship experience was challenging because this was probably the most difficult research I have ever done, I came in with hopes of improving the previous usage of GNNs used in image analysis, but I soon realized how difficult it really was and ended up coming short of my goals. -Srihas Motati
Blog Post
Enhancing Immune Cell Identification
with Graph Neural Networks
SOHAN ARIGELA and SRIHAS MOTATI
Understanding the Complexity of Immune Cells
The immune system, with its diverse cell types and intricate interactions, is essential in
defending the body against diseases. White blood cells, among the most critical components of this system, play a key role in identifying and neutralizing threats. However, the complexity of these cells' spatial organization within tissues has long posed challenges for researchers. Traditional methods for analyzing immune cells involve manual examination of tissue samples, a process that is both time-consuming and susceptible to human error. As the need for precision in understanding diseases like cancer grows, so does the demand for more advanced tools that can provide deeper insights into immune cell behavior.The Need for Advanced Automation in Immune Cell Analysis
Accurate identification and mapping of immune cells in tissue samples are vital for advancing research in areas like immunotherapy, autoimmune disorders, and cancer diagnostics. However, the manual analysis of these cells is labor-intensive and often inconsistent, which can hinder the scalability of research and clinical applications. As a result, there is a pressing need for automated, objective, and scalable methods that can improve the reliability and speed of immune cell analysis.
Our Approach: Leveraging Graph Neural Networks for Immune Cell Identification
To address these challenges, our project focuses on developing a deep learning algorithm using Graph Neural Networks (GNNs) to accurately and efficiently identify immune cells within tissue samples. Unlike other approaches that may rely on multiple technologies such as Optical Character Recognition (OCR), Convolutional Neural Networks (CNNs), or frameworks like Detectron2, our method utilizes only GNNs. This decision is based on the unique ability of
GNNs to model the complex relationships between cells in a spatial context, making them particularly well-suited for this task.
The core idea is to treat each cell as a node in a graph, with edges representing the spatial or contextual relationships between these cells. By analyzing these graphs, GNNs can capture the intricate patterns and interactions within immune cell clusters, leading to more accurate identification of specific cell types, such as white blood cells.
Data Collection: Building a Robust Dataset
Our work is grounded in a comprehensive dataset drawn from 36 patients with Stage-pT3 colorectal cancer, collected at Dartmouth-Hitchcock Medical Center between 2016 and 2019.
These samples were meticulously prepared and scanned into high-resolution whole slide images (WSIs), which were then subdivided into tens of thousands of smaller images. This extensive dataset allows our GNN model to be trained on a wide variety of examples, enhancing its ability to generalize across different tissue samples and improving its accuracy in identifying immune cells.
Evaluating the Performance of GNNs
To measure the effectiveness of our GNN-based approach, we use a variety of performance metrics, including accuracy, F1-score, Area Under the Receiver Operating Characteristic (AUROC), and Intersection Over Union (IOU). These metrics provide a comprehensive view of how well our model performs in identifying immune cells, allowing us to identify strengths and areas for improvement. By focusing on these metrics, we aim to ensure that our model not only works well in controlled settings but also has the potential to be applied in real-world clinical and research environments.
Overcoming Challenges and Moving Forward
One of the significant challenges we face is ensuring that our GNN model is both accurate and efficient, particularly given the complexity of the data and the time constraints associated with our research. However, by focusing on a single, specialized technology—GNNs—we believe we can create a model that is not only powerful but also easier to refine and optimize. Our ultimate goal is to develop a prototype that can serve as a foundation for future research, providing a reliable tool for scientists and clinicians working on immune cell analysis.
Deliverables: Sharing Our Insights and Tools
Our project will culminate in the creation of a prototype simulation of our GNN-based model, along with a detailed blog post documenting our research journey and findings. Additionally, we will develop a user-friendly interface that allows others to explore and test our model. These deliverables are designed to make our work accessible to a broad audience, from researchers and clinicians to anyone interested in the cutting-edge of medical imaging technology.
Conclusion: Pioneering New Frontiers in Immune Cell Analysis
Our project represents a significant advancement in the automation of immune cell analysis. By leveraging the unique strengths of Graph Neural Networks, we aim to create a tool that not only speeds up the analysis process but also enhances its accuracy and reliability. As we continue to refine our model, we hope that our work will contribute to a deeper understanding of immune responses and improve the diagnosis and treatment of diseases, particularly in the field of cancer research and immunotherapy.
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