miraj0507
commented
1 year ago I am thrilled to receive your project request! Your idea is truly fascinating and I am eager to see it come to life.
To ensure the project runs smoothly, please follow all guidelines provided. If you are working on an AI, ML, or DL project, we kindly ask that you create a folder for your project within the respective folders and submit your progress accordingly. Please do follow the project readme template
To ensure that everyone has a fair chance to participate, we kindly request that you complete 75% of the work within the first week of receiving the issue, and the remaining 25% within the next 3 days(10 days in total). If for any reason, you fail to meet this deadline, we will assign the task to someone else who is equally enthusiastic about contributing to this project.
If you have any questions, feel free to contact me via email or Discord or reach out to our team of project mentors.
Thank you for your contribution and let's make this project a huge success!
Anurag9492722884
ghost
Project Request
This project involves using deep learning and computer vision techniques to develop an algorithm to identify metastatic cancer in small image patches obtained from larger digital pathology scans. The dataset using for this project will be comprising of Positive Cell Adenocarcinoma Margin (PCAM) images.
https://github.com/Anurag9492722884
Define You
Project Name
CanDetect
Description
-This project uses deep learning in PyTorch and computer vision techniques to develop an algorithm to identify metastatic cancer in small image patches obtained from larger digital pathology scans.
-The project leverages pre-trained Convolutional Neural Networks (CNNs) and transfer learning to improve the model's performance.
Scope
-Early Cancer Detection: One of the primary objectives of cancer image detection is to identify potential cancerous regions at an early stage. By utilizing deep learning models built with PyTorch, the project can help analyze medical images, such as early signs of cancerous growths or anomalies.
-Accurate Diagnosis: Deep learning models trained on large datasets can learn complex patterns and features that may be difficult for human experts to detect. By leveraging PyTorch's capabilities, the project can develop models capable of accurately diagnosing cancer.
-Research and Development: The project can contribute to the ongoing research and development efforts in the field of cancer diagnosis and treatment.
Timeline
Start time: Time of Assignment End time: July 15th.