Extension to fastai for volumetric medical data

Related tags

Deep Learningfaimed3d
Overview

FAIMED 3D

use fastai to quickly train fully three-dimensional models on radiological data

Classification

from faimed3d.all import *

Load data in various medical formats (DICOM, NIfTI, NRRD) or even videos as simple as in fastai.

d = pd.read_csv('../data/radiopaedia_cases.csv')
dls = ImageDataLoaders3D.from_df(d,
                                 item_tfms = Resize3D((20, 112, 112)),
                                 batch_tfms = aug_transforms_3d(), 
                                 bs = 2, val_bs = 2)

Faimed3d provides multiple model architectures, pretrained on the UCF101 dataset for action recoginiton, which can be used for transfer learning.

Model 3-fold accuracy duration/epoch model size
efficientnet b0 92.5 % 9M:35S 48.8 MB
efficientnet b1 90.1 % 13M:20S 80.5 MB
resnet 18 87.6 % 6M:57S 339.1 MB
resnet 50 94.8 % 12M:16S 561.2 MB
resnet 101 96.0 % 17M:20S 1,030 MB 
# slow
learn = cnn_learner_3d(dls, efficientnet_b0) 

# slow
learn.lr_find()

SuggestedLRs(lr_min=0.014454397559165954, lr_steep=6.309573450380412e-07)

png

Click here for a more in-depth classification example.

Segmentation

dls = SegmentationDataLoaders3D.from_df(d,
                                 item_tfms = Resize3D((20, 112, 112)),
                                 batch_tfms = aug_transforms_3d(), 
                                 bs = 2, val_bs = 2)

All models in faimed3d can be used as a backbone for U-Nets, even with pre-trained weights.

# slow
learn = unet_learner_3d(dls, efficientnet_b0, n_out = 2) 

# slow 
learn.lr_find()

SuggestedLRs(lr_min=0.33113112449646, lr_steep=0.10000000149011612)

png

Click here for a more in-depth segmentation example.

Owner
Keno
Assistant professor of radiology interested in deep learning
Keno
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