Deep Residual Learning based on ResNet50 for COVID-19 Recognition in Lung CT Images

Ferjaoui, Radhia; Cherni, Mohamed Ali; Abidi, Fathia; Zidi, A.

doi:10.1109/codit55151.2022.9804094

Cited by 9 publications

(1 citation statement)

References 18 publications

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“…As the backbone of our architecture, we leverage ResNet-50/ 101 (Ferjaoui et al, 2022) models pretrained on the ImageNet dataset (Russakovsky et al, 2015). To adapt these models for our task, we remove the final fully connected layer.…”

Section: Implementation Detailmentioning

confidence: 99%

A new framework for improving semantic segmentation in aerial imagery

He,

Jin,

Shu

et al. 2024

Front. Remote Sens.

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High spatial resolution (HSR) remote sensing imagery presents a rich tapestry of foreground-background intricacies, rendering semantic segmentation in aerial contexts a formidable and vital undertaking. At its core, this challenge revolves around two pivotal questions: 1) Mitigating Background Interference and Enhancing Foreground Clarity. 2) Accurate Segmentation in Dense Small Object Cluster. Conventional semantic segmentation methods primarily cater to the segmentation of large-scale objects in natural scenes, yet they often falter when confronted with aerial imagery’s characteristic traits such as vast background areas, diminutive foreground objects, and densely clustered targets. In response, we propose a novel semantic segmentation framework tailored to overcome these obstacles. To address the first challenge, we leverage PointFlow modules in tandem with the Foreground-Scene (F-S) module. PointFlow modules act as a barrier against extraneous background information, while the F-S module fosters a symbiotic relationship between the scene and foreground, enhancing clarity. For the second challenge, we adopt a dual-branch structure termed disentangled learning, comprising Foreground Precedence Estimation and Small Object Edge Alignment (SOEA). Our foreground saliency guided loss optimally directs the training process by prioritizing foreground examples and challenging background instances. Extensive experimentation on the iSAID and Vaihingen datasets validates the efficacy of our approach. Not only does our method surpass prevailing generic semantic segmentation techniques, but it also outperforms state-of-the-art remote sensing segmentation methods.

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Section: Implementation Detailmentioning

confidence: 99%

A new framework for improving semantic segmentation in aerial imagery

He,

Jin,

Shu

et al. 2024

Front. Remote Sens.

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A vision transformer‐based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma

Chen,

Luo,

Hou

et al. 2024

Medical Physics

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BackgroundLymph node metastasis (LNM) plays a crucial role in the management of lung cancer; however, the ability of chest computed tomography (CT) imaging to detect LNM status is limited.PurposeThis study aimed to develop and validate a vision transformer‐based deep transfer learning nomogram for predicting LNM in lung adenocarcinoma patients using preoperative unenhanced chest CT imaging.MethodsThis study included 528 patients with lung adenocarcinoma who were randomly divided into training and validation cohorts at a 7:3 ratio. The pretrained vision transformer (ViT) was utilized to extract deep transfer learning (DTL) feature, and logistic regression was employed to construct a ViT‐based DTL model. Subsequently, the model was compared with six classical convolutional neural network (CNN) models. Finally, the ViT‐based DTL signature was combined with independent clinical predictors to construct a ViT‐based deep transfer learning nomogram (DTLN).ResultsThe ViT‐based DTL model showed good performance, with an area under the curve (AUC) of 0.821 (95% CI, 0.775–0.867) in the training cohort and 0.825 (95% CI, 0.758–0.891) in the validation cohort. The ViT‐based DTL model demonstrated comparable performance to classical CNN models in predicting LNM, and the ViT‐based DTL signature was then used to construct ViT‐based DTLN with independent clinical predictors such as tumor maximum diameter, location, and density. The DTLN achieved the best predictive performance, with AUCs of 0.865 (95% CI, 0.827–0.903) and 0.894 (95% CI, 0845–0942), respectively, surpassing both the clinical factor model and the ViT‐based DTL model (p < 0.001).ConclusionThis study developed a new DTL model based on ViT to predict LNM status in lung adenocarcinoma patients and revealed that the performance of the ViT‐based DTL model was comparable to that of classical CNN models, confirming that ViT was viable for deep learning tasks involving medical images. The ViT‐based DTLN performed exceptionally well and can assist clinicians and radiologists in making accurate judgments and formulating appropriate treatment plans.

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DialogueINAB: an interaction neural network based on attitudes and behaviors of interlocutors for dialogue emotion recognition

Ding

Chen

et al. 2023

J Supercomput

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Deep Residual Learning based on ResNet50 for COVID-19 Recognition in Lung CT Images

Cited by 9 publications

References 18 publications

A new framework for improving semantic segmentation in aerial imagery

A new framework for improving semantic segmentation in aerial imagery

A vision transformer‐based deep transfer learning nomogram for predicting lymph node metastasis in lung adenocarcinoma

DialogueINAB: an interaction neural network based on attitudes and behaviors of interlocutors for dialogue emotion recognition

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