Robust COVID-19 Detection in CT Images with CLIP
- URL: http://arxiv.org/abs/2403.08947v3
- Date: Sun, 8 Sep 2024 04:36:57 GMT
- Title: Robust COVID-19 Detection in CT Images with CLIP
- Authors: Li Lin, Yamini Sri Krubha, Zhenhuan Yang, Cheng Ren, Thuc Duy Le, Irene Amerini, Xin Wang, Shu Hu,
- Abstract summary: Deep learning models face challenges in medical imaging, particularly for COVID-19 detection.
We introduce the first lightweight detector designed to overcome these obstacles, leveraging a frozen CLIP image encoder and a trainable multilayer perception (MLP)
We integrate a teacher-student framework to capitalize on the vast amounts of unlabeled data, enabling our model to achieve superior performance despite the inherent data limitations.
- Score: 21.809469794865887
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data. In this work, we introduce the first lightweight detector designed to overcome these obstacles, leveraging a frozen CLIP image encoder and a trainable multilayer perception (MLP). Enhanced with Conditional Value at Risk (CVaR) for robustness and a loss landscape flattening strategy for improved generalization, our model is tailored for high efficacy in COVID-19 detection. Furthermore, we integrate a teacher-student framework to capitalize on the vast amounts of unlabeled data, enabling our model to achieve superior performance despite the inherent data limitations. Experimental results on the COV19-CT-DB dataset demonstrate the effectiveness of our approach, surpassing baseline by up to 10.6% in `macro' F1 score in supervised learning. The code is available at https://github.com/Purdue-M2/COVID-19_Detection_M2_PURDUE.
Related papers
- An Ensemble Deep Learning Approach for COVID-19 Severity Prediction
Using Chest CT Scans [8.512389316218943]
We present our findings on COVID-19 severity prediction from chest CT scans.
We developed an ensemble deep learning based model that incorporates multiple neural networks to improve predictions.
arXiv Detail & Related papers (2023-05-17T10:43:15Z) - Performance of GAN-based augmentation for deep learning COVID-19 image
classification [57.1795052451257]
The biggest challenge in the application of deep learning to the medical domain is the availability of training data.
Data augmentation is a typical methodology used in machine learning when confronted with a limited data set.
In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set.
arXiv Detail & Related papers (2023-04-18T15:39:58Z) - Boosting Automatic COVID-19 Detection Performance with Self-Supervised
Learning and Batch Knowledge Ensembling [38.65823547986758]
Existing methods usually use supervised transfer learning from natural images as a pretraining process.
We introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning.
Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly.
arXiv Detail & Related papers (2022-12-19T07:39:26Z) - COVID-19 Detection Using Transfer Learning Approach from Computed
Tomography Images [0.0]
We propose a transfer learning-based approach using a recently annotated Computed Tomography (CT) image database.
Specifically, we investigate the suitability of a modified Xception model for COVID-19 detection.
Results reveal the method's superiority in accuracy, precision, recall, and macro F1 score on the validation subset.
arXiv Detail & Related papers (2022-07-01T08:22:00Z) - CNN Filter Learning from Drawn Markers for the Detection of Suggestive
Signs of COVID-19 in CT Images [58.720142291102135]
We propose a method that does not require either large annotated datasets or backpropagation to estimate the filters of a convolutional neural network (CNN)
For a few CT images, the user draws markers at representative normal and abnormal regions.
The method generates a feature extractor composed of a sequence of convolutional layers, whose kernels are specialized in enhancing regions similar to the marked ones.
arXiv Detail & Related papers (2021-11-16T15:03:42Z) - The pitfalls of using open data to develop deep learning solutions for
COVID-19 detection in chest X-rays [64.02097860085202]
Deep learning models have been developed to identify COVID-19 from chest X-rays.
Results have been exceptional when training and testing on open-source data.
Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
arXiv Detail & Related papers (2021-09-14T10:59:11Z) - Learning Invariant Representations across Domains and Tasks [81.30046935430791]
We propose a novel Task Adaptation Network (TAN) to solve this unsupervised task transfer problem.
In addition to learning transferable features via domain-adversarial training, we propose a novel task semantic adaptor that uses the learning-to-learn strategy to adapt the task semantics.
TAN significantly increases the recall and F1 score by 5.0% and 7.8% compared to recently strong baselines.
arXiv Detail & Related papers (2021-03-03T11:18:43Z) - Towards Unbiased COVID-19 Lesion Localisation and Segmentation via
Weakly Supervised Learning [66.36706284671291]
We propose a data-driven framework supervised by only image-level labels to support unbiased lesion localisation.
The framework can explicitly separate potential lesions from original images, with the help of a generative adversarial network and a lesion-specific decoder.
arXiv Detail & Related papers (2021-03-01T06:05:49Z) - RCoNet: Deformable Mutual Information Maximization and High-order
Uncertainty-aware Learning for Robust COVID-19 Detection [12.790651338952005]
The novel 2019 Coronavirus (COVID-19) infection has spread world widely and is currently a major healthcare challenge around the world.
Due to faster imaging time and considerably lower cost than CT, detecting COVID-19 in chest X-ray (CXR) images is preferred for efficient diagnosis, assessment and treatment.
We propose a novel deep network named em RCoNet$k_s$ for robust COVID-19 detection which employs em Deformable Mutual Information Maximization (DeIM), em Mixed High-order Moment Feature (MHMF) and em Multi-
arXiv Detail & Related papers (2021-02-22T15:13:42Z) - End-2-End COVID-19 Detection from Breath & Cough Audio [68.41471917650571]
We demonstrate the first attempt to diagnose COVID-19 using end-to-end deep learning from a crowd-sourced dataset of audio samples.
We introduce a novel modelling strategy using a custom deep neural network to diagnose COVID-19 from a joint breath and cough representation.
arXiv Detail & Related papers (2021-01-07T01:13:00Z) - Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image
Translation [6.22964000148682]
We build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach.
We show considerable performance improvements on COVID-19 detection using various deep learning architectures.
Our publicly available benchmark dataset consists of 21,295 synthetic COVID-19 chest X-ray images.
arXiv Detail & Related papers (2020-10-20T13:37:40Z)
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.