CellSignal-Cell-Image-Analysis

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CellSignal-Cell-Image-Analysis

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Disentangling biological signal from experimental noise in cellular images.

// repository documentation

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## DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learningg(Genomics, Proteomics and Bioinformatics)

[ArXiv](https://arxiv.org/abs/2209.05772) | [Cite](#reference)

### kaggle NIPS2019 Recursion Cellular Image Classification Challenge 2nd place [code](https://www.kaggle.com/c/recursion-cellular-image-classification)

#### Dependencies
- imgaug == 0.2.8
- opencv-python==3.4.2
- scikit-image==0.14.0
- scikit-learn==0.19.1
- scipy==1.1.0
- torch==1.0.1.
- torchvision==0.2.2

## Solution Development

#### Input and preprocessing:
- 6 channel input, image size 512*512
- per image standardization: normalization of 6 channels in images per plate, with small randomization
- augmentation: random flip, random rotation multiple of 90 degrees.

#### Backbone CNN:
- Xception and Xception_large;
- Xception is trained from imagenet pretrained weights.
- Xception_large is trained from srcatch which need more trainning epochs.

#### Loss fuction:
- Arcface Loss s=30, m=0.1:

#### Path Setup
Set the following path to your own in ./setting.py
```
data_dir = r'your-own-path/recursion-cellular-image-classification'#data path
```

#### Single Model Training
pretrain xception_large 512x512 fold 0 with all cell types：
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --mode=pretrain --model=xception_large --image_size=512 --fold_index=0 --batch_size=64
```

finetune xception_large 512x512 fold 0 on each cell type：
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --mode=semi_finetune --model=xception_large --image_size=512 --fold_index=0 --batch_size=64
```

predict xception_large 512x512 fold 0 model：
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --mode=infer --model=xception_large --image_size=512 --fold_index=0 --batch_size=64
```

#### generate prediction

```
python ensemble.py
```

## Reference
If you find our work useful in your research or if you use parts of this code please consider citing our paper:

```@article{WANG2021102785,
title = {DeepNoise: Signal and Noise Disentanglement based on Classifying Fluorescent Microscopy Images via Deep Learning},
author={Sen,Yang and Tao,Shen and Yuqi,Fang and Xiyue,Wang and Jun,Zhang and Wei,Yang and Junzhou,Huang and Xiao,Han},
journal = {Genomics, Proteomics and Bioinformatics},  
year = {2023}  
```

CellSignal-Cell-Image-Analysis

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