Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19

@article{Vidal2021MultistageTL,
  title={Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19},
  author={Pl{\'a}cido Francisco Lizancos Vidal and J. D. Moura and J. Novo and Marcos Ortega},
  journal={Expert Systems with Applications},
  year={2021},
  volume={173},
  pages={114677 - 114677}
}
A Novel Multistage Transfer Learning for Ultrasound Breast Cancer Image Classification
TLDR
It is argued that learning from both natural and medical datasets improves performance in ultrasound breast cancer image classification and could remarkably improve the early diagnosis of breast cancer in young women.
Does imbalance in chest X-ray datasets produce biased deep learning approaches for COVID-19 screening?
TLDR
An analysis of patient characteristics like sex and age in pathologies of this type in chest X-ray images to identify biases evidenced that the proposed methodology and tested approaches allow a robust and reliable analysis to support the clinical decision-making process in this pandemic scenario.
Patchless Multi-Stage Transfer Learning for Improved Mammographic Breast Mass Classification
TLDR
A novel deep-learning method based on multi-stage transfer learning (MSTL) from ImageNet and cancer cell line image pre-trained models to classify mammographic masses as either benign or malignant, which performs better than patch- and whole image-based methods for early diagnosis of breast cancer.
A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images
TLDR
An approach based on the evaluation of the histogram from a common class of images that is considered as the target, which shows that, at least when the images of the considered datasets are homogeneous enough, it is not really needed to resort to complex-to-implement DL techniques, in order to attain an effective detection of the COVID-19 disease.
Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability
TLDR
The care need in the different patient groupings is analyzed, a personalized care suggestion system by applying RNN models is proposed, and an efficient model training scheme for building AI-assisted prediction models is developed.
Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review
TLDR
A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy, providing confidence that the highest results were not driven by biased papers.
COVID-19 Lung Radiography Segmentation by Means of Multiphase Transfer Learning
TLDR
A COVID-19-specific methodology able to segment these portable chest radiographs with a reduced number of samples via multiple transfer learning phases is presented to help both experts and other computer-aided diagnosis systems to focus their attention on the region of interest, ignoring unrelated information.
...
1
2
...

References

SHOWING 1-10 OF 87 REFERENCES
Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning
TLDR
The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible.
Deep convolutional approaches for the analysis of Covid-19 using chest X-Ray images from portable devices
TLDR
Novel fully automatic approaches specifically tailored for the classification of chest X-Ray images acquired by portable equipment into 3 different clinical categories: normal, pathological and COVID-19 are presented.
Fully automatic deep convolutional approaches for the analysis of Covid-19 using chest X-ray images
TLDR
This work proposes complementary fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases, and exploits and adapted to this topic a densely convolutional network architecture.
Residual Attention U-Net for Automated Multi-Class Segmentation of COVID-19 Chest CT Images
TLDR
This study uses the Aggregated Residual Transformations to learn a robust and expressive feature representation and applies the soft attention mechanism to improve the capability of the model to distinguish a variety of symptoms of the COVID-19.
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19
TLDR
This review paper covers the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up, and particularly focuses on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals.
COVID-19 Chest CT Image Segmentation - A Deep Convolutional Neural Network Solution
TLDR
Inspired by the observation that the boundary of the infected lung can be enhanced by adjusting the global intensity, in the proposed deep CNN, a feature variation block is introduced which adaptively adjusts the global properties of the features for segmenting COVID-19 infection.
Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine
TLDR
The suggested system of multi-level thresholding plus SVM presented high accuracy in classification of the infected lung with Covid-19, and the deep studying based totally methodology is usually recommended for the detection of COVID-19 infected patients using X-ray images.
Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
TLDR
A novel COVID-19 Lung Infection Segmentation Deep Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices and outperforms most cutting-edge segmentation models and advances the state-of-the-art performance.
...
1
2
3
4
5
...