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--- |
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name: "K-POP" |
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license: "mit" |
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metrics: |
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- MAE |
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- PLCC |
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- SRCC |
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- R2 |
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tags: |
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- focus-prediction |
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- microscopy |
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- pytorch |
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--- |
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# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning |
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<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a><a href="https://pytorchlightning.ai/"> |
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<img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white"></a> |
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<a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/Config-Hydra-89b8cd"></a> |
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## Description |
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This repository contains the models and training pipeline for my master thesis. The main repository is hosted on [GitHub](https://github.com/13hannes11/master_thesis_code). |
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The project structure is based on the template by [ashleve](https://github.com/ashleve/lightning-hydra-template). |
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The metadata is stored in `data/focus150/`. The relevant files are `test_metadata.csv`, `train_metadata.csv` and `validation_metadata.csv`. Image data (of 150 x 150 px images) is not published together with this repository therefore training runs are not possible to do without it. The layout of the metadata files is as follows |
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```csv |
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,image_path,scan_uuid,study_id,focus_height,original_filename,stack_id,obj_name |
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0,31/b0d4005e-57d0-4516-a239-abe02a8d0a67/I02413_X009_Y014_Z5107_750_300.jpg,b0d4005e-57d0-4516-a239-abe02a8d0a67,31,-0.013672000000000017,I02413_X009_Y014_Z5107.jpg,1811661,schistosoma |
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1,31/274d8969-aa7c-4ac0-be60-e753579393ad/I01981_X019_Y014_Z4931_450_0.jpg,274d8969-aa7c-4ac0-be60-e753579393ad,31,-0.029296999999999962,I01981_X019_Y014_Z4931.jpg,1661371,schistosoma |
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... |
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``` |
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## How to run |
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Train model with chosen experiment configuration from `configs/experiment/` |
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```bash |
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python train.py experiment=focusResNet_150 |
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``` |
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Train with hyperparameter search from `configs/hparams_search/` |
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```bash |
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python train.py -m hparams_search=focusResNetMSE_150 |
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``` |
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You can override any parameter from command line like this |
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```bash |
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python train.py trainer.max_epochs=20 datamodule.batch_size=64 |
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``` |
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## Jupyter notebooks |
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Figures and other evaluation code was run in Jupyter notebooks. These are available at `notebooks/` |
