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)

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)

Click here for a more in-depth segmentation example.

Extension to fastai for volumetric medical data

Related tags

Overview

FAIMED 3D

Classification

Segmentation

Owner

Keno

A Benchmark For Measuring Systematic Generalization of Multi-Hierarchical Reasoning

Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems

Learning Multiresolution Matrix Factorization and its Wavelet Networks on Graphs

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

Hierarchical Uniform Manifold Approximation and Projection

Multiple-Object Tracking with Transformer

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Very Deep Convolutional Networks for Large-Scale Image Recognition

Sleep staging from ECG, assisted with EEG

Video Frame Interpolation without Temporal Priors (a general method for blurry video interpolation)

OpenMMLab Text Detection, Recognition and Understanding Toolbox

Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle

Self-Learning - Books Papers, Courses & more I have to learn soon

This project demonstrates the use of neural networks and computer vision to create a classifier that interprets the Brazilian Sign Language.

This repo implements several applications of the proposed generalized Bures-Wasserstein (GBW) geometry on symmetric positive definite matrices.

Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization

Efficient neural networks for analog audio effect modeling

Panoptic SegFormer: Delving Deeper into Panoptic Segmentation with Transformers

PyKaldi GOP-DNN on Epa-DB

Pytorch re-implementation of Paper: SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition (CVPR 2022)