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

KSAI Lite is a deep learning inference framework of kingsoft, based on tensorflow lite

Simple and understandable swin-transformer OCR project

Federated Learning Based on Dynamic Regularization

Anime Face Detector using mmdet and mmpose

A library for Deep Learning Implementations and utils

GestureSSD CBAM - A gesture recognition web system based on SSD and CBAM, using pytorch, flask and node.js

DPT: Deformable Patch-based Transformer for Visual Recognition (ACM MM2021)

Adversarial Reweighting for Partial Domain Adaptation

Dense Gaussian Processes for Few-Shot Segmentation

Part-aware Measurement for Robust Multi-View Multi-Human 3D Pose Estimation and Tracking

🔀 Visual Room Rearrangement

Softlearning is a reinforcement learning framework for training maximum entropy policies in continuous domains. Includes the official implementation of the Soft Actor-Critic algorithm.

Restricted Boltzmann Machines in Python.

Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Auditing Black-Box Prediction Models for Data Minimization Compliance

Res2Net for Instance segmentation and Object detection using MaskRCNN

BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation

基于Paddlepaddle复现yolov5，支持PaddleDetection接口

Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification

Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.