lupus83/panic

Code of "Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease", published at IPMI23

Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease

This repository contains the code to the paper "Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease"

@inproceedings{Wolf2023-IPMI,
  doi = {10.1007/978-3-031-34048-2_7},
  author = {Wolf, Tom Nuno and P{\"o}lsterl, Sebastian and Wachinger, Christian},
  title = {Don't PANIC: Prototypical Additive Neural Network for Interpretable Classification of Alzheimer's Disease},
  booktitle = {Information Processing in Medical Imaging},
  pages = {82--94},
  year = {2023}
}

If you are using this code, please cite the paper above.

Installation

Use conda to create an environment called panic with all dependencies:

conda env create -n panic --file requirements.yaml

Additionally, install the package torchpanic from this repository with

pip install --no-deps -e .

Data

We used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Since we are not allowed to share our data, you would need to process the data yourself. Data for training, validation, and testing should be stored in separate HDF5 files, using the following hierarchical format:

1. First level: A unique identifier.
2. The second level always has the following entries:
   1. A group named PET with the subgroup FDG, which itself has the dataset named data as child: The FDG-PET volume of size (113,117,113). Additionally, the subgroup FDG has an attribute imageuid with is the unique image identifier.
   2. A group named tabular, which has two datasets called data and missing, each of size 41: data contains the tabular data values, while missing is a missing value indicator if a tabular feature was not acquired at this visit.
   3. A scalar attribute RID with the patient ID.
   4. A string attribute VISCODE with ADNI's visit code.
   5. A string attribute DX containing the diagnosis (CN, MCI or Dementia).

One entry in the resulting HDF5 file should have the following structure:

/1010012                 Group
    Attribute: RID scalar
        Type:      native long
        Data:  1234
    Attribute: VISCODE scalar
        Type:      variable-length null-terminated UTF-8 string
        Data:  "bl"
    Attribute: DX scalar
        Type:      variable-length null-terminated UTF-8 string
        Data:  "CN"
/1010012/PET Group
/1010012/PET/FDG Group
    Attribute imageuid scalar
        Type:      variable-length null-terminated UTF-8 string
        Data: "12345"
/1010012/PET/FDG/data Dataset {113, 137, 133}
/1010012/tabular Group
/1010012/tabular/data Dataset {41}
/1010012/tabular/missing Dataset {41}

Finally, the HDF5 file should also contain the following meta-information in a separate group named stats:

/stats/tabular           Group
/stats/tabular/columns   Dataset {41}
/stats/tabular/mean      Dataset {41}
/stats/tabular/stddev    Dataset {41}

They are the names of the features in the tabular data, their mean, and standard deviation.

Usage

PANIC processes tabular data depending on its data type. Therefore, it is necessary to tell PANIC how to process each tabular feature: The following indices must be given to the model in the configs file configs/model/panic.yaml:

idx_real_features: indices of real-valued features within tabular data. idx_cat_features: indices of categorical features within tabular data. idx_real_has_missing: indices of real-valued features which should be considered from missing. idx_cat_has_missing: indices of categorical features which should be considered from missing.

Similarly, missing tabular inputs to DAFT (configs/model/daft.yaml) need to be specified with idx_tabular_has_missing.

Training

To train PANIC, or any of the baseline models, adapt the config files (mainly train.yaml) and execute the train.py script to begin training.

Model checkpoints will be written to the outputs folder by default.

Interpretation of results

We provide some useful utility function to create plots and visualization required to interpret the model. You can find them under torchpanic/viz.