Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Related tags

Deep Learningild-cnn
Overview

ild-cnn

This is supplementary material for the manuscript:

"Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network"
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe and S. Mougiakakou
IEEE Transactions on Medical Imaging (2016)
http://dx.doi.org/10.1109/TMI.2016.2535865

In case of any questions, please do not hesitate to contact us.

Environment:

This code was used on a Linux machine with Ubuntu (14.04.3 LTS) using the following setup:

Component Description:

There are three major components

  • main.py : the main script which parses the train parameters, loads some sample data and runs the training of the CNN.
  • helpers.py : a file with some helper functions for parsing the input parameters, loading sample data and calculating a number of evaluation metrics
  • cnn_model.py : this file implements the architecture of the proposed CNN and trains it.

How to use:

python main.py : runs an experiment with the default parameters
python main.py -h : shows the help message

Output Description:

The execution outputs two csv formatted files with the performance metrics of the CNN. The first contains the performances for each training epoch while the second only for the epochs that improved the performance. The code prints the same output while running as well as a confusion matrix every time the CNN performance improves.

Disclaimer:

Copyright (C) 2016 Marios Anthimopoulos, Stergios Christodoulidis, Stavroula Mougiakakou / University of Bern

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

[NeurIPS-2021] Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data

MosaicKD Code for NeurIPS-21 paper "Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data" 1. Motivation Natural images share common l

ZJU-VIPA 37 Nov 10, 2022
Colab notebook and additional materials for Python-driven analysis of redlining data in Philadelphia

RedliningExploration The Google Colaboratory file contained in this repository contains work inspired by a project on educational inequality in the Ph

Benjamin Warren 1 Jan 20, 2022
You Only Look One-level Feature (YOLOF), CVPR2021, Detectron2

You Only Look One-level Feature (YOLOF), CVPR2021 A simple, fast, and efficient object detector without FPN. This repo provides a neat implementation

qiang chen 273 Jan 03, 2023
Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021)

EMI-FGSM This repository contains code to reproduce results from the paper: Boosting Adversarial Attacks with Enhanced Momentum (BMVC 2021) Xiaosen Wa

John Hopcroft Lab at HUST 10 Sep 26, 2022
Uni-Fold: Training your own deep protein-folding models.

Uni-Fold: Training your own deep protein-folding models. This package provides and implementation of a trainable, Transformer-based deep protein foldi

DeepModeling 88 Jan 03, 2023
Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks.

FDRL-PC-Dyspan Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks. This repository contains the entire code

Peyman Tehrani 17 Nov 18, 2022
Towards the D-Optimal Online Experiment Design for Recommender Selection (KDD 2021)

Towards the D-Optimal Online Experiment Design for Recommender Selection (KDD 2021) Contact 0 Jan 11, 2022

Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite.

TFLite-HITNET-Stereo-depth-estimation Python scripts form performing stereo depth estimation using the HITNET model in Tensorflow Lite. Stereo depth e

Ibai Gorordo 22 Oct 20, 2022
This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

0 Feb 02, 2022
Pytorch implementation of OCNet series and SegFix.

openseg.pytorch News 2021/09/14 MMSegmentation has supported our ISANet and refer to ISANet for more details. 2021/08/13 We have released the implemen

openseg-group 1.1k Dec 23, 2022
Official implementation of the paper Momentum Capsule Networks (MoCapsNet)

Momentum Capsule Network Official implementation of the paper Momentum Capsule Networks (MoCapsNet). Abstract Capsule networks are a class of neural n

8 Oct 20, 2022
根据midi文件演奏“风物之诗琴”的脚本 "Windsong Lyre" auto play

Genshin-lyre-auto-play 简体中文 | English 简介 根据midi文件演奏“风物之诗琴”的脚本。由Python驱动,在此承诺, ⚠️ 项目内绝不含任何能够引起安全问题的代码。 前排提示:所有键盘在动但是原神没反应的都是因为没有管理员权限,双击run.bat或者以管理员模式

御坂17032号 386 Jan 01, 2023
This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object Tracking with TRansformer.

MOTR: End-to-End Multiple-Object Tracking with TRansformer This repository is an official implementation of the paper MOTR: End-to-End Multiple-Object

348 Jan 07, 2023
FuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space OptimizationFuseDream: Training-Free Text-to-Image Generationwith Improved CLIP+GAN Space Optimization

FuseDream This repo contains code for our paper (paper link): FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space Optimizat

XCL 191 Dec 31, 2022
Pytorch implementation of AREL

Status: Archive (code is provided as-is, no updates expected) Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement

8 Nov 25, 2022
Pytorch library for fast transformer implementations

Transformers are very successful models that achieve state of the art performance in many natural language tasks

Idiap Research Institute 1.3k Dec 30, 2022
Graph Analysis From Scratch

Graph Analysis From Scratch Goal In this notebook we wanted to implement some functionalities to analyze a weighted graph only by using algorithms imp

Arturo Ghinassi 0 Sep 17, 2022
GLIP: Grounded Language-Image Pre-training

GLIP: Grounded Language-Image Pre-training Updates 12/06/2021: GLIP paper on arxiv https://arxiv.org/abs/2112.03857. Code and Model are under internal

Microsoft 862 Jan 01, 2023
Finetune SSL models for MOS prediction

Finetune SSL models for MOS prediction This is code for our paper under review for ICASSP 2022: "Generalization Ability of MOS Prediction Networks" Er

Yamagishi and Echizen Laboratories, National Institute of Informatics 32 Nov 22, 2022
Oriented Object Detection: Oriented RepPoints + Swin Transformer/ReResNet

Oriented RepPoints for Aerial Object Detection The code for the implementation of “Oriented RepPoints + Swin Transformer/ReResNet”. Introduction Based

96 Dec 13, 2022