Data Preparation Steps
1.	Extracting Images from Archives:
o	We use Python's zipfile module to extract the images from CrossValidation.zip and HoldOut.zip.
o	Depending on the analysis level (patch-level or patient-level), we extract either Annotated.zip or Cropped.zip from CrossValidation.zip.
o	The extracted images are stored in EXTRACT_annotated or EXTRACT_cropped directories.
2.	Organizing Images:
o	After extraction, images are located inside nested directories named after patient IDs.
o	We rename and move the images to flatten the directory structure, facilitating easier access during model training.
o	Images are renamed using a combination of patient names and sequence numbers.
3.	Creating Usable Image Sets:
o	We apply filtering criteria to select images suitable for training.
o	For patch-level images, we match the image filenames with entries in HP_WSI-CoordAnnotatedPatches.xlsx to ensure that only images with valid annotations are included.
o	For patient-level images, we use the density information from PatientDiagnosis_cropped.csv to select patients with specific characteristics.
4.	Generating Annotation CSV Files:
o	We process the Excel and CSV annotation files to create structured CSV files for training.
o	In TRAIN_DATA_annotated.csv, we ensure that each image has corresponding patch-level annotations.
o	For patient-level data in TRAIN_DATA_cropped.csv, we map the DENSITAT field to binary values (0 or 1), making it suitable for binary classification.
5.	Data Cleaning and Filtering:
o	We handle missing values, incorrect entries, and ensure consistency across all data files.
o	For example, in the patch-level data, we remove patches without valid Presence values.
o	We also balance the dataset by considering the number of images per patient and per class.
Data Exploration
Before model development, we perform thorough data exploration to understand the dataset's characteristics. This involves:
o	Assessing Class Distribution:
o	We analyze the distribution of classes in both patch-level and patient-level data.
o	Understanding class imbalance is crucial for selecting appropriate loss functions and evaluation metrics.
o	Visual Examination of Images:
o	Sample images from different classes are visualized to gain insights into the data.
o	This helps in identifying any anomalies or patterns that could affect model performance.
o	Statistical Analysis:
o	We compute basic statistics such as the number of images per patient, the number of patches per class, and the distribution of annotations.
o	This information guides the data augmentation strategies and model hyperparameter tuning.
o	Data Integrity Checks:
o	We verify the correctness of image filenames, annotations, and ensure that there is a correct mapping between images and their labels.

Model Selection and Training
We provide an option to choose between two training approaches: classification and detection. This choice is facilitated through a user input where selecting 0 initiates the classifier training, and selecting 1 would initiate the detection model 


As for the classifier, we focus on building a model that can accurately classify images based on the provided annotations.
Steps Involved:
1.	Loading and Transforming Data:
o	The images from USABLE_annotated are loaded using custom data loading functions.
o	We use a transformation pipeline to resize images and convert them to tensors suitable for input into neural networks.
2.	Dataset Creation:
o	We create a dataset class (StandardImageDataset) that pairs images with their corresponding labels from TRAIN_DATA_annotated.csv.
o	This dataset is then used to create data loaders for batching and shuffling during training.
3.	Model Configuration:
o	We define a range of configurations for the classifier models, experimenting with different architectures and hyperparameters.
o	Pre-trained models like ResNet50 and DenseNet201 are utilized as backbones for feature extraction.
o	Custom fully connected layers are added on top of the backbone models to adapt them to our specific classification task.
4.	Defining Loss Function and Optimizer:
o	We use cross-entropy loss with class weights to handle any class imbalance.
o	The optimizer used is Adam with an appropriate learning rate.
5.	Training Loop:
o	The training process involves iterating over epochs and batches, forward passing through the network, computing loss, and backpropagating to update weights.
o	We record training loss, accuracy, and recall at each epoch for monitoring.
6.	Model Evaluation:
o	After training, models are evaluated on a validation set to assess performance.
o	Metrics such as validation loss, accuracy, and recall are calculated.
7.	Model Saving and Logging:
o	Trained models are saved for future inference or further analysis.
o	Training curves are plotted and saved to visualize the training progress.
By allowing experimentation with various model configurations, we aim to identify the architecture and hyperparameters that yield the best performance on our dataset.


CAN YOU REDACT WITHOUT USING BULLET LISTS AND WITH A MORE PRECISE AND HIGH LEVEL LANGUAGE? SPECIFYING WHY WE CHOOSE IMPORTANT PARAMETERS