The official repository for "Revealing unforeseen diagnostic image features with deep learning by detecting cardiovascular diseases from apical four-chamber ultrasounds"

Overview

Revealing unforeseen diagnostic image features with deep learning by detecting cardiovascular diseases from apical four-chamber ultrasounds

image In this project, we aimed to develop a deep learning (DL) method to automatically detect impaired left ventricular (LV) function and aortic valve (AV) regurgitation from apical four-chamber (A4C) ultrasound cineloops. Two R(2+1)D convolutional neural networks (CNNs) were trained to detect the respective diseases. Subsequently, tSNE was used to visualize the embedding of the extracted feature vectors, and DeepLIFT was used to identify important image features associated with the diagnostic tasks.

The why

  • An automated echocardiography interpretation method requiring only limited views as input, say A4C, could make cardiovascular disease diagnosis more accessible.

    • Such system could become beneficial in geographic regions with limited access to expert cardiologists and sonographers.
    • It could also support general practitioners in the management of patients with suspected CVD, facilitating timely diagnosis and treatment of patients.
  • If the trained CNN can detect the diseases based on limited information, how?

    • Especially, AV regurgitation is typically diagnosed based on color Doppler images using one or more viewpoints. When given only the A4C view, would the model be able to detect regurgitation? If so, what image features does the model use to make the distinction? Since it’s on the A4C view, would the model identify some anatomical structure or movement associated with regurgitation, which are typically not being considered in conventional image interpretation? This is what we try to find out in the study.

Image features associated with the diagnostic tasks

DeepLIFT attributes a model’s classification output to certain input features (pixels), which allows us to understand which region or frame in an ultrasound is the key that makes the model classify it as a certain diagnosis. Below are some example analyses.

Representative normal cases

Case Averaged logit Input clip / Impaired LV function model's focus / AV regurgitation model's focus
Normal1 0.9999 image
Normal2 0.9999 image
Normal3 0.9999 image
Normal4 0.9999 image
Normal5 0.9999 image
Normal6 0.9999 image
Normal7 0.9998 image
Normal8 0.9998 image
Normal9 0.9998 image
Normal10 0.9997 image

DeepLIFT analyses reveal that the LV myocardium and mitral valve were important for detecting impaired LV function, while the tip of the mitral valve anterior leaflet, during opening, was considered important for detecting AV regurgitation. Apart from the above examples, all confident cases are provided, which the predicted probability of being the normal class by the two models are both higher than 0.98. See the full list here.

Representative disease cases

  • Mildly impaired LV
Case Logit Input clip / Impaired LV function model's focus
MildILV1 0.9989 image
MildILV2 0.9988 image
  • Severely impaired LV
Case Logit Input clip / Impaired LV function model's focus
SevereILV1 1.0000 image
SevereILV2 1.0000 image
  • Mild AV regurgitation
Case Logit Input clip / AV regurgitation model's focus
MildAVR1 0.7240 image
MildAVR2 0.6893 image
  • Substantial AV regurgitation
Case Logit Input clip / AV regurgitation model's focus
SubstantialAVR1 0.9919 image
SubstantialAVR2 0.9645 image

When analyzing disease cases, the highlighted regions in different queries are quite different. We speculate that this might be due to a higher heterogeneity in the appearance of the disease cases. Apart from the above examples, more confident disease cases are provided. See the full list here.

Run the code on your own dataset

The dataloader in util can be modified to fit your own dataset. To run the full workflow, namely training, validation, testing, and the subsequent analyses, simply run the following commands:

git clone https://github.com/LishinC/Disease-Detection-and-Diagnostic-Image-Feature.git
cd Disease-Detection-and-Diagnostic-Image-Feature/util
pip install -e .
cd ../projectDDDIF
python main.py

Loading the trained model weights

The model weights are made available for external validation, or as pretraining for other echocardiography-related tasks. To load the weights, navigate to the projectDDDIF folder, and run the following python code:

import torch
import torch.nn as nn
import torchvision

#Load impaired LV model
model_path = 'model/impairedLV/train/model_val_min.pth'
# #Load AV regurgitation model
# model_path = 'model/regurg/train/model_val_min.pth'

model = torchvision.models.video.__dict__["r2plus1d_18"](pretrained=False)
model.stem[0] = nn.Conv3d(1, 45, kernel_size=(1, 7, 7), stride=(1, 2, 2), padding=(0, 3, 3), bias=False)
model.fc = nn.Linear(model.fc.in_features, 3)
model.load_state_dict(torch.load(model_path))

Questions and feedback

For techinical problems or comments about the project, feel free to contact [email protected].

Official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch.

Multi-speaker DGP This repository provides official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch. O

sarulab-speech 24 Sep 07, 2022
This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,

labml.ai Deep Learning Paper Implementations This is a collection of simple PyTorch implementations of neural networks and related algorithms. These i

labml.ai 16.4k Jan 09, 2023
Implementation of Google Brain's WaveGrad high-fidelity vocoder

WaveGrad Implementation (PyTorch) of Google Brain's high-fidelity WaveGrad vocoder (paper). First implementation on GitHub with high-quality generatio

Ivan Vovk 363 Dec 27, 2022
Stacked Generative Adversarial Networks

Stacked Generative Adversarial Networks This repository contains code for the paper "Stacked Generative Adversarial Networks", CVPR 2017. Part of the

Xun Huang 241 May 07, 2022
Code for the published paper : Learning to recognize rare traffic sign

Improving traffic sign recognition by active search This repo contains code for the paper : "Learning to recognise rare traffic signs" How to use this

samsja 4 Jan 05, 2023
Pytorch code for "State-only Imitation with Transition Dynamics Mismatch" (ICLR 2020)

This repo contains code for our paper State-only Imitation with Transition Dynamics Mismatch published at ICLR 2020. The code heavily uses the RL mach

20 Sep 08, 2022
Code for Neurips2021 Paper "Topology-Imbalance Learning for Semi-Supervised Node Classification".

Topology-Imbalance Learning for Semi-Supervised Node Classification Introduction Code for NeurIPS 2021 paper "Topology-Imbalance Learning for Semi-Sup

Victor Chen 40 Nov 23, 2022
GLANet - The code for Global and Local Alignment Networks for Unpaired Image-to-Image Translation arxiv

GLANet The code for Global and Local Alignment Networks for Unpaired Image-to-Image Translation arxiv Framework: visualization results: Getting Starte

stanley 29 Dec 14, 2022
CC-GENERATOR - A python script for generating CC

CC-GENERATOR A python script for generating CC NOTE: This tool is for Educationa

Lêkzï 6 Oct 14, 2022
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

Real-ESRGAN Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data Ported from https://github.com/xinntao/Real-ESRGAN Depend

Holy Wu 44 Dec 27, 2022
[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks

Large Scale Image Completion via Co-Modulated Generative Adversarial Networks, ICLR 2021 (Spotlight) Demo | Paper [NEW!] Time to play with our interac

Shengyu Zhao 373 Jan 02, 2023
CCPD: a diverse and well-annotated dataset for license plate detection and recognition

CCPD (Chinese City Parking Dataset, ECCV) UPdate on 10/03/2019. CCPD Dataset is now updated. We are confident that images in subsets of CCPD is much m

detectRecog 1.8k Dec 30, 2022
Diffgram - Supervised Learning Data Platform

Data Annotation, Data Labeling, Annotation Tooling, Training Data for Machine Learning

Diffgram 1.6k Jan 07, 2023
PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"

Contrast to Divide: self-supervised pre-training for learning with noisy labels This is an official implementation of "Contrast to Divide: self-superv

55 Nov 23, 2022
MutualGuide is a compact object detector specially designed for embedded devices

Introduction MutualGuide is a compact object detector specially designed for embedded devices. Comparing to existing detectors, this repo contains two

ZHANG Heng 103 Dec 13, 2022
MultiTaskLearning - Multi Task Learning for 3D segmentation

Multi Task Learning for 3D segmentation Perception stack of an Autonomous Drivin

2 Sep 22, 2022
Object-Centric Learning with Slot Attention

Slot Attention This is a re-implementation of "Object-Centric Learning with Slot Attention" in PyTorch (https://arxiv.org/abs/2006.15055). Requirement

Untitled AI 72 Jan 02, 2023
An implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional Neural Network"

Retina Blood Vessels Segmentation This is an implementation of the research paper "Retina Blood Vessel Segmentation Using A U-Net Based Convolutional

Srijarko Roy 23 Aug 20, 2022
Simple-Image-Classification - Simple Image Classification Code (PyTorch)

Simple-Image-Classification Simple Image Classification Code (PyTorch) Yechan Kim This repository contains: Python3 / Pytorch code for multi-class ima

Yechan Kim 8 Oct 29, 2022
A modular application for performing anomaly detection in networks

Deep-Learning-Models-for-Network-Annomaly-Detection The modular app consists for mainly three annomaly detection algorithms. The system supports model

Shivam Patel 1 Dec 09, 2021