Library containing implementation related to the research paper "Multi-task pre-training of deep neural networks for digital pathology" (Mormont et al.).

It can be used to load our pre-trained models or to build a multi-task classification architecture.

For an example, check the file examples/feature_extract.py.

The library provides a build_model function to build a model and initialize it with our pre-trained weights. To load our weights, the parameter pretrained should be set to mtdp.

from mtdp import build_model

model = build_model(arch="densenet121", pretrained="mtdp")

Alternatively, pretrained can be set to imagenet to load ImageNet pre-trained weights from PyTorch.

We currently provide pre-trained weights for the following architectures:

• densenet121
• resnet50

See an example script performing feature extraction using one of our model in the examples folder (file feature_extract.py).

If you want to bypass the library and download the raw PyTorch model files, you can access them at the following URLs:

• densenet121: https://dox.uliege.be/index.php/s/G72InP4xmJvOrVp/download
• resnet50: https://dox.uliege.be/index.php/s/kvABLtVuMxW8iJy/download

For an example, see the examples/multi_task_train.py file.

Several steps for building the architecture:

1. define a DatasetFolder/ImageFolder for each of your individual dataset,
2. instantiate a MultiImageFolders object with all your dataset objects,
3. instantiate a MultiHead PyTorch module by passing it the MultiImageFolders from step 2. The module will use the information of the tasks in order to build the multi-task architecture.