This repository contains the implementation of a Prostate Cancer Grade Assessment system, aimed at aiding in the accurate grading of prostate cancer tissue samples. The system utilizes machine learning techniques to analyze histopathology images and predict the grade of prostate cancer based on the Gleason score.
Features:
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Histopathology Image Analysis:
- Utilizes deep learning models to analyze digitized histopathology images of prostate tissue samples.
- Extracts relevant features from the images to characterize the morphology and structure of cancerous tissue.
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Gleason Score Prediction:
- Predicts the Gleason score of prostate cancer tissue samples, which is a crucial factor in determining cancer aggressiveness and treatment decisions.
- Employs convolutional neural networks (CNNs) or other deep learning architectures for accurate grade assessment.
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Model Interpretability:
- Implements techniques for model interpretability to understand the factors influencing the grade prediction.
- Provides visualization tools to interpret and explain the model's decisions to pathologists and clinicians.
Usage:
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Training the Model:
- Train the deep learning model using histopathology image datasets labeled with Gleason scores.
- Fine-tune pre-trained models or train from scratch, depending on the available data and computational resources.
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Model Evaluation:
- Evaluate the trained model's performance using validation datasets or cross-validation techniques.
- Assess metrics such as accuracy, precision, recall, and F1-score to measure the model's effectiveness in grade assessment.
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Deployment:
- Deploy the trained model as a scalable and efficient system for automated prostate cancer grade assessment.
- Integrate the system into existing pathology workflows to assist pathologists in clinical decision-making.
Dependencies:
- Python 3.x
- TensorFlow or PyTorch
- OpenCV for image processing
- Scikit-learn for model evaluation
- Matplotlib and Seaborn for visualization