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].

PyExplainer: A Local Rule-Based Model-Agnostic Technique (Explainable AI)

PyExplainer PyExplainer is a local rule-based model-agnostic technique for generating explanations (i.e., why a commit is predicted as defective) of J

AI Wizards for Software Management (AWSM) Research Group 14 Nov 13, 2022
Code for paper entitled "Improving Novelty Detection using the Reconstructions of Nearest Neighbours"

NLN: Nearest-Latent-Neighbours A repository containing the implementation of the paper entitled Improving Novelty Detection using the Reconstructions

Michael (Misha) Mesarcik 4 Dec 14, 2022
Algorithmic Trading using RNN

Deep-Trading This an implementation adapted from Rachnog Neural networks for algorithmic trading. Part One — Simple time series forecasting and this c

Hazem Nomer 29 Sep 04, 2022
Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2

Graph Transformer - Pytorch Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2. This was recently used by bot

Phil Wang 97 Dec 28, 2022
Indonesian Car License Plate Character Recognition using Tensorflow, Keras and OpenCV.

Monopol Indonesian Car License Plate (Indonesia Mobil Nomor Polisi) Character Recognition using Tensorflow, Keras and OpenCV. Background This applicat

Jayaku Briliantio 3 Apr 07, 2022
Cervix ROI Segmentation Using U-NET

Cervix ROI Segmentation Using U-NET Overview This code illustrate how to segment the ROI in cervical images using U-NET. The ROI here meant to include

Scotty Kwok 35 Sep 14, 2022
Deep-learning X-Ray Micro-CT image enhancement, pore-network modelling and continuum modelling

EDSR modelling A Github repository for deep-learning image enhancement, pore-network and continuum modelling from X-Ray Micro-CT images. The repositor

Samuel Jackson 7 Nov 03, 2022
PyTorch implementation for COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction (CVPR 2021)

Completer: Incomplete Multi-view Clustering via Contrastive Prediction This repo contains the code and data of the following paper accepted by CVPR 20

XLearning Group 72 Dec 07, 2022
Meta-Learning Sparse Implicit Neural Representations (NeurIPS 2021)

Meta-SparseINR Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, N

Jaeho Lee 41 Nov 10, 2022
FFCV: Fast Forward Computer Vision (and other ML workloads!)

Fast Forward Computer Vision: train models at a fraction of the cost with accele

FFCV 2.3k Jan 03, 2023
BasicNeuralNetwork - This project looks over the basic structure of a neural network and how machine learning training algorithms work

BasicNeuralNetwork - This project looks over the basic structure of a neural network and how machine learning training algorithms work. For this project, I used the sigmoid function as an activation

Manas Bommakanti 1 Jan 22, 2022
[AAAI-2022] Official implementations of MCL: Mutual Contrastive Learning for Visual Representation Learning

Mutual Contrastive Learning for Visual Representation Learning This project provides source code for our Mutual Contrastive Learning for Visual Repres

winycg 48 Jan 02, 2023
Official PyTorch implementation of the paper "TEMOS: Generating diverse human motions from textual descriptions"

TEMOS: TExt to MOtionS Generating diverse human motions from textual descriptions Description Official PyTorch implementation of the paper "TEMOS: Gen

Mathis Petrovich 187 Dec 27, 2022
Code for technical report "An Improved Baseline for Sentence-level Relation Extraction".

RE_improved_baseline Code for technical report "An Improved Baseline for Sentence-level Relation Extraction". Requirements torch = 1.8.1 transformers

Wenxuan Zhou 74 Nov 29, 2022
MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens

MSG-Transformer Official implementation of the paper MSG-Transformer: Exchanging Local Spatial Information by Manipulating Messenger Tokens, by Jiemin

Hust Visual Learning Team 68 Nov 16, 2022
A concise but complete implementation of CLIP with various experimental improvements from recent papers

x-clip (wip) A concise but complete implementation of CLIP with various experimental improvements from recent papers Install $ pip install x-clip Usag

Phil Wang 515 Dec 26, 2022
Using BERT+Bi-LSTM+CRF

Chinese Medical Entity Recognition Based on BERT+Bi-LSTM+CRF Step 1 I share the dataset on my google drive, please download the whole 'CCKS_2019_Task1

Xiang WU 55 Dec 21, 2022
[Pedestron] Generalizable Pedestrian Detection: The Elephant In The Room. @ CVPR2021

Pedestron Pedestron is a MMdetection based repository, that focuses on the advancement of research on pedestrian detection. We provide a list of detec

Irtiza Hasan 594 Jan 05, 2023
Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

Deepak Nandwani 1 Jan 01, 2022
Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

Molecular Docking integrated in Jupyter Notebooks Description | Citation | Installation | Examples | Limitations | License Table of content Descriptio

Angel J. Ruiz Moreno 173 Dec 25, 2022