Disentangled Generation and Editing of Pathology Images

• Topics: computational pathology, image generation, disentangled representations, latent space manipulation, deep learning
• Skills:
  • Programming Languages:
    • Proficient in Python, with experience in machine learning libraries such as PyTorch or TensorFlow.
  • Generative Models:
    • Familiarity with Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and contrastive learning methods.
  • Data Analysis:
    • Image processing techniques, statistical analysis, and working with histopathology datasets.
  • Biomedical Knowledge (preferred):
    • Basic understanding of histology, cancer pathology, and biological image annotation.
• Difficulty: Advanced
• Size: Large (350 hours). The project involves substantial computational work, model development, and evaluation of generated pathology images.
• Mentors: Xi Li (contact person), Mentor Name

Project Idea Description

The project aims to advance the generation and disentanglement of pathology images, focusing on precise control over key histological features. By leveraging generative models, we seek to create synthetic histological images where specific pathological characteristics can be independently controlled.

Challenges in Current Approaches

Current methods in histopathology image generation often struggle with:

1. Feature Entanglement: Difficulty in isolating individual factors such as cancer presence, severity, or staining variations.
2. Lack of Control: Limited capability to manipulate specific pathological attributes without affecting unrelated features.
3. Consistency Issues: Generated images often fail to maintain realistic cellular distributions, affecting biological validity.

Project Motivation

This project proposes a disentangled representation framework to address these limitations. By separating key features within the latent space, we aim to:

• Control Histological Features: Adjust factors such as cancer presence, tumor grade, number of malignant cells, and staining methods.
• Ensure Spatial Consistency: Maintain the natural distribution of cells during image reconstruction and editing.
• Enable Latent Space Manipulation: Provide interpretable controls for editing and generating realistic histopathology images.

Project Objectives

1. Disentangled Representation Learning:
   • Develop generative models (e.g., VAEs, GANs) to separate and control histological features.
2. Latent Space Manipulation:
   • Design mechanisms for intuitive editing of pathology images through latent space adjustments.
3. Spatial Consistency Validation:
   • Implement evaluation metrics to ensure that cell distribution remains biologically consistent during image generation.

Project Deliverables

1. Generative Model Framework:
   • An open-source Python implementation for pathology image generation and editing.
2. Disentangled Latent Space Tools:
   • Tools for visualizing and manipulating latent spaces to control specific pathological features.
3. Evaluation Metrics:
   • Comprehensive benchmarks assessing image quality, feature disentanglement, and biological realism.
4. Documentation and Tutorials:
   • Clear guidelines and code examples for the research community to adopt and build upon this work.

Impact

By enabling precise control over generated histology images, this project will contribute to data augmentation, model interpretability, and biological insight in computational pathology. The disentangled approach offers new opportunities for researchers to explore disease mechanisms, develop robust diagnostic models, and improve our understanding of cancer progression and tissue morphology.