pytorch-zoom-in-network

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pytorch-zoom-in-network

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Source code for "Efficient Classification of Very Large Images with Tiny Objects"

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This is the official repository for "[Efficient Classification of Very Large Images with Tiny Objects](https://arxiv.org/abs/2106.02694)".

# Overview
An increasing number of applications in computer vision, specially, in medical imaging and remote sensing, become challenging when the goal is to classify very large images with tiny informative objects. 
Specifically, these classification tasks face two key challenges: $i$) the size of the input image is usually in the order of mega- or giga-pixels, however, existing deep architectures do not easily operate on such big images due to memory constraints, consequently, we seek a memory-efficient method to process these images; and $ii$) only a very small fraction of the input images are informative of the label of interest, resulting in low region of interest (ROI) to image ratio.
However, most of the current convolutional neural networks (CNNs) are designed for image classification datasets that have relatively large ROIs and small image sizes (sub-megapixel).
Existing approaches have addressed these two challenges in isolation.
We present an end-to-end CNN model termed Zoom-In network that leverages hierarchical attention sampling for classification of large images with tiny objects using a single GPU.
We evaluate our method on four large-image histopathology, road-scene and satellite imaging datasets, and one gigapixel pathology dataset.
Experimental results show that our model achieves higher accuracy than existing methods while requiring less memory resources.

## Major Dependencies

pytorch-gpu == 1.6.0

torchvision == 0.7.0

## Training
training on colon cancer dataset:

python main.py /path_to_your_dataset/ /path_to_your_output/ --mode 10CrossValidation --model_name yourModelName
    
## Evaluation

python main.py /path_to_your_dataset/ /path_to_your_model_directory/ --mode Evaluation --model_name yourModelName
    
## Acknowledgemennt
This work was supported by NIH (R44-HL140794), DARPA (FA8650-18-2-7832-P00009-12) and ONR (N00014-18-1-2871-P00002-3).

## Research

If you would like to cite our work,

@inproceedings{kong2022efficient,
      title={Efficient Classification of Very Large Images with Tiny Objects},
      author={Kong, Fanjie and Henao, Ricardo},
      booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
      pages={2384--2394},
      year={2022}
    }
    
##### Thanks to the following repositories inpired our work: 
- https://github.com/idiap/attention-sampling.git

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