
# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning

<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/">
<img alt="Lightning" src="https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning&logoColor=white"></a>
<a href="https://hydra.cc/"><img alt="Config: Hydra" src="https://img.shields.io/badge/Config-Hydra-89b8cd"></a>

## Description

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).

The project structure is based on the template by [ashleve](https://github.com/ashleve/lightning-hydra-template).

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

```csv
,image_path,scan_uuid,study_id,focus_height,original_filename,stack_id,obj_name
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
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
...
```


## How to run

Train model with chosen experiment configuration from `configs/experiment/`

```bash
python train.py experiment=focusResNet_150
```

Train with hyperparameter search from `configs/hparams_search/`

```bash
python train.py -m hparams_search=focusResNetMSE_150
```

You can override any parameter from command line like this

```bash
python train.py trainer.max_epochs=20 datamodule.batch_size=64
```

## Jupyter notebooks

Figures and other evaluation code was run in Jupyter notebooks. These are available at `notebooks/`
{"license": "mit", "tags": ["focus-prediction", "microscopy", "pytorch"], "metrics": ["MAE", "PLCC", "SRCC", "R2"], "name": "K-POP"}
tensorboard focus-prediction microscopy pytorch license:mit region:us
TAGS
#tensorboard #focus-prediction #microscopy #pytorch #license-mit #region-us 


# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning

<a href="URL alt="PyTorch" src="URL href="URL
<img alt="Lightning" src="URL
<a href="URL alt="Config: Hydra" src="URL

## Description

This repository contains the models and training pipeline for my master thesis. The main repository is hosted on GitHub.

The project structure is based on the template by ashleve.

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




## How to run

Train model with chosen experiment configuration from 'configs/experiment/'



Train with hyperparameter search from 'configs/hparams_search/'



You can override any parameter from command line like this



## Jupyter notebooks

Figures and other evaluation code was run in Jupyter notebooks. These are available at 'notebooks/'
# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning

<a href="URL alt="PyTorch" src="URL href="URL
<img alt="Lightning" src="URL
<a href="URL alt="Config: Hydra" src="URL ## Description

This repository contains the models and training pipeline for my master thesis. The main repository is hosted on GitHub.

The project structure is based on the template by ashleve.

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 ## How to run

Train model with chosen experiment configuration from 'configs/experiment/'



Train with hyperparameter search from 'configs/hparams_search/'



You can override any parameter from command line like this ## Jupyter notebooks

Figures and other evaluation code was run in Jupyter notebooks. These are available at 'notebooks/'
TAGS
#tensorboard #focus-prediction #microscopy #pytorch #license-mit #region-us 
 # K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning

<a href="URL alt="PyTorch" src="URL href="URL
<img alt="Lightning" src="URL
<a href="URL alt="Config: Hydra" src="URL ## Description

This repository contains the models and training pipeline for my master thesis. The main repository is hosted on GitHub.

The project structure is based on the template by ashleve.

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 ## How to run

Train model with chosen experiment configuration from 'configs/experiment/'



Train with hyperparameter search from 'configs/hparams_search/'



You can override any parameter from command line like this ## Jupyter notebooks

Figures and other evaluation code was run in Jupyter notebooks. These are available at 'notebooks/'
passage: TAGS
#tensorboard #focus-prediction #microscopy #pytorch #license-mit #region-us 
# K-POP: Predicting Distance to Focal Plane for Kato-Katz Prepared Microscopy Slides Using Deep Learning

<a href="URL alt="PyTorch" src="URL href="URL
<img alt="Lightning" src="URL
<a href="URL alt="Config: Hydra" src="URL## Description

This repository contains the models and training pipeline for my master thesis. The main repository is hosted on GitHub.

The project structure is based on the template by ashleve.

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## How to run

Train model with chosen experiment configuration from 'configs/experiment/'



Train with hyperparameter search from 'configs/hparams_search/'



You can override any parameter from command line like this## Jupyter notebooks

Figures and other evaluation code was run in Jupyter notebooks. These are available at 'notebooks/'