Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis

Last update: Dec 6, 2022

Related tags

Deep Learning CSPR

Overview

Introduction

This is an implementation of our paper Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis.

Modified from mmclassification.

Support 3D ResNet pre-training with 2D natural-image dataset.

Installation

Please refer to install.md for installation.

Data preparation

Download ImageNet dataset and put as the following structure:

data
  ├──imagenet
        ├── get_meta.sh
        ├── meta
        │   ├── val.txt
        │   ├── test.txt
        │   ├── train.txt
        ├── val
        │   ├──ILSVRC2012_val_00000001.JPEG
        │   ├──ILSVRC2012_val_00000002.JPEG
        │   ├── ...
        ├── test
        │   ├──ILSVRC2012_test_00000001.JPEG
        │   ├──ILSVRC2012_test_00000002.JPEG
        │   ├── ...
        └── train
              └── n10148035
              │    ├── n10148035_10034.JPEG
              │    ├── n10148035_10371.JPEG
              │    ├── ...
              └── n11879895
              │    ├── ...
              └── ...

Pre-train a 3D model on ImageNet dataset

Run this script to pre-train a 3D-ResNet-18 model on ImageNet dataset. It will take around 7 days on 8 Titan XP GPUs.

bash pre_train.sh

Pre-trained Model

We provide models pre-trained on ImageNet dataset which can be used for different 3D medical image analysis tasks.

The pre-trained 3D-ResNet-18 model can be downloaded from Google drive or BaiduYun(verification code: 9hgg).

Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation (ICCV2021)

Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation (ICCV2021) This is the implementation of PSD (ICCV 2021),

12 Dec 12, 2022

Build a medical knowledge graph based on Unified Language Medical System (UMLS)

UMLS-Graph Build a medical knowledge graph based on Unified Language Medical System (UMLS) Requisite Install MySQL Server 5.6 and import UMLS data int

6 Dec 25, 2022

Open-AI's DALL-E for large scale training in mesh-tensorflow.

DALL-E in Mesh-Tensorflow [WIP] Open-AI's DALL-E in Mesh-Tensorflow. If this is similarly efficient to GPT-Neo, this repo should be able to train mode

432 Dec 16, 2022

An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicity.

Fast Face Classification (F²C) This is the code of our paper An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicit

33 Jun 27, 2021

A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation mode

36 Oct 30, 2022

A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ASPset-510 (Australian Sports Pose Dataset) is a large-scale video dataset for the training and evaluation of 3D human pose estimation models. It contains 17 different amateur subjects performing 30 sports-related actions each, for a total of 510 action clips.

25 Jun 20, 2021

ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge (ManiSkill Challenge), a large-scale learning-from-demonstrations benchmark for object manipulation.

ManiSkill-Learn ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge, a large-scale learning-from-dem

48 Dec 30, 2022

Galileo library for large scale graph training by JD

近年来，图计算在搜索、推荐和风控等场景中获得显著的效果，但也面临超大规模异构图训练，与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo（伽利略）是一个图深度学习框架，具备超大规模、易使用、易扩展、高性能、双后端等优点，旨在解决超大规模图算法在工业级场景的落地难题，提

128 Nov 29, 2022

Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

ColossalAI An integrated large-scale model training system with efficient parallelization techniques Installation PyPI pip install colossalai Install

7.1k Jan 3, 2023

Comments

CVE-2007-4559 Patch

Patching CVE-2007-4559

Hi, we are security researchers from the Advanced Research Center at Trellix. We have began a campaign to patch a widespread bug named CVE-2007-4559. CVE-2007-4559 is a 15 year old bug in the Python tarfile package. By using extract() or extractall() on a tarfile object without sanitizing input, a maliciously crafted .tar file could perform a directory path traversal attack. We found at least one unsantized extractall() in your codebase and are providing a patch for you via pull request. The patch essentially checks to see if all tarfile members will be extracted safely and throws an exception otherwise. We encourage you to use this patch or your own solution to secure against CVE-2007-4559. Further technical information about the vulnerability can be found in this blog.

If you have further questions you may contact us through this projects lead researcher Kasimir Schulz.

opened by TrellixVulnTeam 0

Supervised 3D Pre-training on Large-scale 2D Natural Image Datasets for 3D Medical Image Analysis

Related tags

Overview

Introduction

Installation

Data preparation

Pre-train a 3D model on ImageNet dataset

Pre-trained Model

You might also like...

Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Segmentation (ICCV2021)

Build a medical knowledge graph based on Unified Language Medical System (UMLS)

Open-AI's DALL-E for large scale training in mesh-tensorflow.

An Efficient Training Approach for Very Large Scale Face Recognition or F²C for simplicity.

A large-scale video dataset for the training and evaluation of 3D human pose estimation models

A large-scale video dataset for the training and evaluation of 3D human pose estimation models

ManiSkill-Learn is a framework for training agents on SAPIEN Open-Source Manipulation Skill Challenge (ManiSkill Challenge), a large-scale learning-from-demonstrations benchmark for object manipulation.

Galileo library for large scale graph training by JD

Colossal-AI: A Unified Deep Learning System for Large-Scale Parallel Training

Comments

CVE-2007-4559 Patch

Patching CVE-2007-4559

Owner

Official repository for the paper, MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding.

Large-Scale Pre-training for Person Re-identification with Noisy Labels (LUPerson-NL)

BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training

The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.

Official implementation of "Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets" (CVPR2021)

[CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

Unified Pre-training for Self-Supervised Learning and Supervised Learning for ASR

Cl datasets - PyTorch image dataloaders and utility functions to load datasets for supervised continual learning

Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation"

This is a Pytorch implementation of the paper: Self-Supervised Graph Transformer on Large-Scale Molecular Data.