MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic
Retinopathy Severity Prediction
- URL: http://arxiv.org/abs/2208.06917v1
- Date: Sun, 14 Aug 2022 20:55:13 GMT
- Title: MTCSNN: Multi-task Clinical Siamese Neural Network for Diabetic
Retinopathy Severity Prediction
- Authors: Chao Feng, Jui Po Hung, Aishan Li, Jieping Yang, Xinyu Zhang
- Abstract summary: We propose a novel design MTCSNN, a Multi-task Clinical Siamese Neural Network for Diabetic Retinopathy severity prediction task.
The novelty of this project is to utilize the ordinal information among labels and add a new regression task, which can help the model learn more discriminative feature embedding for fine-grained classification tasks.
Our results indicate that MTCSNN outperforms the benchmark models in terms of AUC and accuracy on the test dataset.
- Score: 13.7701794591862
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Diabetic Retinopathy (DR) has become one of the leading causes of vision
impairment in working-aged people and is a severe problem worldwide. However,
most of the works ignored the ordinal information of labels. In this project,
we propose a novel design MTCSNN, a Multi-task Clinical Siamese Neural Network
for Diabetic Retinopathy severity prediction task. The novelty of this project
is to utilize the ordinal information among labels and add a new regression
task, which can help the model learn more discriminative feature embedding for
fine-grained classification tasks. We perform comprehensive experiments over
the RetinaMNIST, comparing MTCSNN with other models like ResNet-18, 34, 50. Our
results indicate that MTCSNN outperforms the benchmark models in terms of AUC
and accuracy on the test dataset.
Related papers
- GARNN: An Interpretable Graph Attentive Recurrent Neural Network for
Predicting Blood Glucose Levels via Multivariate Time Series [12.618792803757714]
We propose interpretable graph attentive neural networks (GARNNs) to model multi-modal data.
GARNNs achieve the best prediction accuracy and provide high-quality temporal interpretability.
These findings underline the potential of GARNN as a robust tool for improving diabetes care.
arXiv Detail & Related papers (2024-02-26T01:18:53Z) - Neural scaling laws for phenotypic drug discovery [3.076170146656896]
We investigate if scale can have a similar impact for models designed to aid small molecule drug discovery.
We find that DNNs explicitly supervised to solve tasks in the Pheno-CA do not continuously improve as their data and model size is scaled-up.
We introduce a novel precursor task, the Inverse Biological Process (IBP), which is designed to resemble the causal objective functions that have proven successful for NLP.
arXiv Detail & Related papers (2023-09-28T18:10:43Z) - Transferability of coVariance Neural Networks and Application to
Interpretable Brain Age Prediction using Anatomical Features [119.45320143101381]
Graph convolutional networks (GCN) leverage topology-driven graph convolutional operations to combine information across the graph for inference tasks.
We have studied GCNs with covariance matrices as graphs in the form of coVariance neural networks (VNNs)
VNNs inherit the scale-free data processing architecture from GCNs and here, we show that VNNs exhibit transferability of performance over datasets whose covariance matrices converge to a limit object.
arXiv Detail & Related papers (2023-05-02T22:15:54Z) - Continuous time recurrent neural networks: overview and application to
forecasting blood glucose in the intensive care unit [56.801856519460465]
Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations.
We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
arXiv Detail & Related papers (2023-04-14T09:39:06Z) - Predicting Brain Age using Transferable coVariance Neural Networks [119.45320143101381]
We have recently studied covariance neural networks (VNNs) that operate on sample covariance matrices.
In this paper, we demonstrate the utility of VNNs in inferring brain age using cortical thickness data.
Our results show that VNNs exhibit multi-scale and multi-site transferability for inferring brain age
In the context of brain age in Alzheimer's disease (AD), our experiments show that i) VNN outputs are interpretable as brain age predicted using VNNs is significantly elevated for AD with respect to healthy subjects.
arXiv Detail & Related papers (2022-10-28T18:58:34Z) - Investigating the Predictive Reproducibility of Federated Graph Neural
Networks using Medical Datasets [0.0]
We present the first work investigating the application of federated GNN models with application to classifying medical imaging and brain connectivity datasets.
We showed that federated learning boosts both the accuracy and accuracy of GNN models in such medical learning tasks.
arXiv Detail & Related papers (2022-09-13T14:32:03Z) - N-Omniglot: a Large-scale Neuromorphic Dataset for Spatio-Temporal
Sparse Few-shot Learning [10.812738608234321]
We provide the first neuromorphic dataset: N- Omniglot, using the Dynamic Vision Sensor (DVS)
It contains 1623 categories of handwritten characters, with only 20 samples per class.
The dataset provides a powerful challenge and a suitable benchmark for developing SNNs algorithm in the few-shot learning domain.
arXiv Detail & Related papers (2021-12-25T12:41:34Z) - Medulloblastoma Tumor Classification using Deep Transfer Learning with
Multi-Scale EfficientNets [63.62764375279861]
We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions.
Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
arXiv Detail & Related papers (2021-09-10T13:07:11Z) - CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors
and Efficient Neural Networks [51.589769497681175]
The novel coronavirus (SARS-CoV-2) has led to a pandemic.
The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands.
We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
arXiv Detail & Related papers (2020-07-20T21:47:28Z) - Predicting Clinical Diagnosis from Patients Electronic Health Records
Using BERT-based Neural Networks [62.9447303059342]
We show the importance of this problem in medical community.
We present a modification of Bidirectional Representations from Transformers (BERT) model for classification sequence.
We use a large-scale Russian EHR dataset consisting of about 4 million unique patient visits.
arXiv Detail & Related papers (2020-07-15T09:22:55Z) - DRDr: Automatic Masking of Exudates and Microaneurysms Caused By
Diabetic Retinopathy Using Mask R-CNN and Transfer Learning [2.0559497209595823]
We make use of Convolutional Neural Networks (CNNs) and Transfer Learning to locate and generate high-quality segmentation mask.
We create our normalized database out of e-ophtha EX and e-ophtha MA and tweak Mask R-CNN to detect small lesions.
Our model achieves promising test mAP of 0.45, altogether showing that it can aid clinicians and ophthalmologist in the process of detecting and treating the infamous DR.
arXiv Detail & Related papers (2020-07-04T07:20:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.