# **Insights into the behavior of multi-task deep neural networks for medical image segmentation - Reproducibility Package**
This repository is a reproducibility package for the paper "Insights into the behavior of multi-task deep neural networks for medical image segmentation” published
in: 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). Paper has can be found [here](https://ieeexplore.ieee.org/document/8918753)
Reproducibility package consists two main parts. First one is devotedt to Mask R-CNN architecture, while the second one to SA-FCN architecture.
Provided code allows to reproduce all tables and figures placed in the paper, as well as experiment with the training, predicting and post-processing methods.
-[Introduction](#introduction)
-[Mask R-CNN](#maskrcnn)
-[SA-FCN](#safcn)
-[Cite Paper](#cite-uva)
-[Installation, tutorials and documentation](#installation-tutorials-and-documentation)
Glandular morphology is used by pathologists to assess the malignancy of different adenocarcinomas. This process involves conducting gland segmentation task. The common
approach in specialised domains, such as medical imaging, is to design complex architectures in a multi-task learning setup. Generally, these approaches rely on
substantial postprocessing efforts. Moreover, a predominant notion is that general purpose models are not suitable for gland instance segmentation. We analyse the
behaviour of two architectures: SA-FCN and Mask R-CNN. We compare the impact of post-processing on the final predictive results and the performance of generic and
specific models for the gland segmentation problem. Our results highlight the dependency of post-processing on tailored models as well as comparable results when using
a generic model. Thus, in the interest of time, it is worth considering to use and improve generic models as opposed to design complex architectures when tackling new
If you use the package, please cite the following works:
```
L. T. Bienias, J. R. Guillamón, L. H. Nielsen and T. S. Alstrøm, "Insights Into The Behaviour Of Multi-Task Deep Neural Networks For Medical Image Segmentation," 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Pittsburgh, PA, USA, 2019, pp. 1-6.
```
Bibtex entry:
```
@inproceedings{bienias2019insights,
title={Insights into the behaviour of multi-task deep neural networks for medical image segmentation},
author={Bienias, Lukasz T and Nielsen, Line H and Alstr{\o}m, Tommy S and others},
booktitle={2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP)},
pages={1--6},
year={2019},
organization={IEEE}
}
```
For more information about our research group please visit [Section for Cognitive Systems website](https://www.compute.dtu.dk/english/research/research-sections/cogsys)
at [Technical University of Denamrk](https://www.dtu.dk//)(Denmark).
We are interested in feedback and error reporting. Please contact us via email or open an issue in the repository if you have any kind of problem, comment, suggestion or
The Mask R-CNN architecture is described in the [article](https://arxiv.org/abs/1703.06870).
In the article we used implementation written in Tensorflow framework, which comes from the [repository](https://github.com/matterport/Mask_RCNN). The code has been
The SA-FCN architecture is described in the [article](https://arxiv.org/abs/1706.04737).
The package consists of implementation written in Pytorch framework, following the information included in the article as well as the Lua implementation,
