Deep Learning for Morphological Profiling

Overview

Deep Learning for Morphological Profiling

An end-to-end implementation of a ML System for morphological profiling using self-supervised learning to disantangle the biological signal from experimental noise in biological microscopy images.

ML System Stack

Data Layer

Training/Evaluation

  • Pytorch and PytorchLightning
  • AWS EC2 for compute
  • WandB for Experiment tracking and Hyperparameter Optimization
  • Docker for resource management

Deployment

  • FastAPI
  • CircleCI
  • AWS Lambda

Status Update

Data

  • Load Data script
  • Preprocess images
  • Data module
  • Data Validation for metadata csv file

Training

  • Train resnet baseline
  • Setup wandb for experiment tracking and hyperparameter optimization
  • evaluation metrics
  • ConvNeXt modified for 6 channels
  • DenseNet 161
  • Pretext task to predict rotation

Testing

  • linting, syntax, data types.
  • full training cycle
  • input, outputs shapes
  • single batch and epoch
  • functionality test: load pretraind and predict with sample examples
  • evaluation test

Deployment

  • Setup FastAPI
  • CircleCI for CI/CD
  • input and output handlers
  • Server script

Monitoring & Observability

  • system health & performance
  • rule-based stats(min, max, mean, std)
  • data drift(mean pixel value, using projections)
  • Setup evaluation store
Owner
Danielh Carranza
Biomedical & Machine Learning Engineer and Entrepreneur.
Danielh Carranza
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