Sote.AI

AI-Powered Diagnostics

Sote.AI

Sote.AI is our innovative diagnostic tool that utilizes AI to detect cancer at early stages using ultrasound data and biomarker samples. Our goal is to make early cancer detection accessible and affordable.

Strategic Implementation Plan for HCC and PAR Diagnosis System

This plan outlines a detailed approach for each stage of the proposed hybrid architecture, focusing on pre-processing, feature extraction, model architecture, hyperparameter optimization, dimensionality reduction, texture analysis, and activation functions.

Stage 1: Pre-processing and Feature Extraction

1.1 Data Augmentation with Generative Adversarial Networks (GANs)
  • Implementation: Utilize TensorFlow or PyTorch to implement a Conditional GAN (CGAN).
  • Training Data: Collect diverse ultrasound datasets for HCC and PAR, including both labeled and unlabeled data. Collaborate with research institutions or hospitals.
  • CGAN Architecture: Design a CGAN with a generator network that outputs synthetic ultrasound images with HCC or PAR characteristics, and a discriminator network to differentiate real from synthetic images.
  • Training Process: Train the CGAN iteratively, using techniques like Wasserstein GAN with gradient penalty (WGAN-GP) for stable training.
  • Output: The trained CGAN generates synthetic ultrasound images to augment the existing dataset.
1.2 Attention-based Feature Extraction with Transformers
  • Implementation: Utilize a pre-trained vision transformer (ViT) model. Fine-tune it on the combined dataset (original + synthetic images).
  • Fine-tuning Process: Freeze earlier layers and train final layers on HCC and PAR classification. Employ Adam optimizer and learning rate scheduling.
  • Output: Fine-tuned ViT extracts high-level features from ultrasound images for HCC and PAR detection.

Stage 2: Deep Learning Model (Hybrid)

2.1 Hierarchical Transformer with Graph Convolutional Network (GCN)
  • Architecture: Feed the fine-tuned ViT into a hierarchical transformer and a GCN. Concatenate outputs and feed into a final classification layer.
  • Hierarchical Transformer: Design with multiple encoder-decoder stages. Utilize self-attention mechanisms.
  • Graph Convolutional Network (GCN): Represent the ultrasound image as a graph. Design a GCN for spatial context.
  • Training Process: Train end-to-end with techniques like Adam optimizer, weighted sum of classification losses, and curriculum learning.

Stage 3: Hyperparameter Optimization and Activation Functions

3.1 Hyperparameter Optimization:
  • Techniques: Utilize Optuna or Ray Tune for hyperparameter optimization. Implement grid or random search.
  • Evaluation Metric: Use accuracy, F1-score, or AUC-ROC for model evaluation.
3.2 Activation Functions:
  • Fine-tuned ViT: Use ReLU or Leaky ReLU activations. Experiment with SiLU or GELU in final layers.
  • Hierarchical Transformer: Employ ReLU or Leaky ReLU activations. Consider Swish or Mish for deeper networks.
  • Graph Convolutional Network (GCN): Use ReLU or Leaky ReLU activations.

Stage 4: Validation, Deployment, and Scalability

4.1 Validation:
  • Validation Set: Assess model performance on unseen data using metrics like accuracy, F1-score, precision, recall, and AUC-ROC.
  • Cross-Validation: Consider k-fold cross-validation for robust evaluation.
4.2 Deployment:
  • On-Premise Deployment: Deploy on your own server if feasible.
  • AWS Elastic Container Service (ECS): Package the model in a Docker container for deployment on AWS ECS.
4.3 Scalability:
  • Data-Parallel Training: Explore data-parallel training for limited computational resources.
  • Model Compression: Utilize techniques like quantization or pruning.
  • Federated Learning (Future Consideration): Consider federated learning for data sharing while maintaining privacy.

Stage 5: Continuous Learning and Improvement

  • Monitor model performance and collect new data.
  • Re-train periodically with new data to improve accuracy and generalization.
  • Explore transfer learning or curriculum learning for performance gains.