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+Robust blood Detection Model through weakly supervised localization, self-supervised pretraining, adversarial training, 3D convolutions, and video frame modeling.
+Overview
+This repository provides a PyTorch deep neural network for classifying microscope images of blood cells as benign or malignant. The model achieves high accuracy and generalizability by leveraging:
+Weakly supervised localization to identify explanatory regions used by the classifier via class activation mappings
+Self-supervised pretraining on unlabeled blood cell video data to prime feature extraction layers
+Test-time adversarial training to improve model robustnes to small input perturbations
+3D convolutions to analyze volumetric shape cues rather than flattened 2D images
+Video frame order modeling for additional temporal self-supervision
+Combined, these techniques improve predictive performance while providing intepretability.
+
+-Installation
+This code requires Python 3.8+ and Poetry for dependency management. Install dependencies with:
+
+poetry install
+
+-Activate the Poetry environment for usage:
+
+poetry shell
+
+-Usage
+To train the model on the blood_cell_videos dataset:
+
+python training/trainer.py --data-dir /path/to/blood_cells --epochs 100 --lr 0.001
+
+This will configure the neural architecture, leverage unlabeled videos for self-supervision, and fit the model parameters on the labeled dataset.
+
+-Contributing
+Contributions to improving the weakly supervised and self-supervised components are greatly welcome! Please open issues for any bugs or desired functionality.
+
+-License
+This project is licensed under the MIT license. See LICENSE.md for details.
+Let me know if you would like any additional sections or more information added!