Trying to create a novel way of predicting prostate cancer for the challenge PROSTATEx Challenge 2017 using Conditional Random Fields
- Lapa, P.; Castelli, M.; Gonçalves, I.; Sala, E.; Rundo, L. A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI. Appl. Sci. 2020, 10, 338. https://doi.org/10.3390/app10010338
- Paulo Lapa, Ivo Gonçalves, Leonardo Rundo, and Mauro Castelli. 2019. Semantic learning machine improves the CNN-Based detection of prostate cancer in non-contrast-enhanced MRI. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19). Association for Computing Machinery, New York, NY, USA, 1837–1845. DOI:https://doi.org/10.1145/3319619.3326864
- Paulo Lapa, Ivo Gonçalves, Leonardo Rundo, and Mauro Castelli. 2019. Enhancing classification performance of convolutional neural networks for prostate cancer detection on magnetic resonance images: a study with the semantic learning machine. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19). Association for Computing Machinery, New York, NY, USA, 381–382. DOI:https://doi.org/10.1145/3319619.3322035
- Conditional random fields improve the CNN-based prostate cancer classification performance, Master Thesis, 2019
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience