API Documentation

All modules above listed are under the “medicaltorch” namespace.

Contents:

medicaltorch.datasets – Datasets

medicaltorch.transforms – Transformations

medicaltorch.metrics – Metrics

medicaltorch.metrics.numeric_score(prediction, groundtruth)

Computation of statistical numerical scores:

• FP = False Positives
• FN = False Negatives
• TP = True Positives
• TN = True Negatives

return: tuple (FP, FN, TP, TN)

medicaltorch.models – Models

class medicaltorch.models.NoPoolASPP(drop_rate=0.4, bn_momentum=0.1, base_num_filters=64)

An ASPP-based model without initial pooling layers.

Parameters:

• drop_rate – dropout rate.
• bn_momentum – batch normalization momentum.

See also

Perone, C. S., et al (2017). Spinal cord gray matter segmentation using deep dilated convolutions. Nature Scientific Reports link: https://www.nature.com/articles/s41598-018-24304-3

forward(x)

Model forward pass.

Parameters:x – input data.

class medicaltorch.models.Unet(drop_rate=0.4, bn_momentum=0.1)

A reference U-Net model.

See also

Ronneberger, O., et al (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation ArXiv link: https://arxiv.org/abs/1505.04597

medicaltorch.losses – Losses

class medicaltorch.losses.MaskedDiceLoss(ignore_value=-100.0)

A masked version of the Dice loss.

Parameters:ignore_value – the value to ignore.

medicaltorch.losses.dice_loss(input, target)

Dice loss.

Parameters:

• input – The input (predicted)
• target – The target (ground truth)

Returns:

the Dice score between 0 and 1.