VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification

Mishra, Suraj; Wang, Ya Xing; Wei, Chuan Chuan; Chen, Danny Z.; Hu, Xiang

doi:10.3389/fmed.2021.750396

Cited by 13 publications

(4 citation statements)

References 24 publications

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“…The aim of the ablation study is to acquire a clear understanding of the model’s performance by analyzing the consequence of altering some components ( 59 ). With the alteration of different components or hyper-parameters of a model, a change in performance is observed ( 60 ). This method can ascertain any potential decrease in the performance of the model, which can later be fixed by updating and tuning the network.…”

Section: Proposed Methodologymentioning

confidence: 99%

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

et al. 2022

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Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.

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Section: Proposed Methodologymentioning

confidence: 99%

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

et al. 2022

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“…Traditional methods fail to fuse vascular topological information. Mishra et al [ 47 ] proposed a new CNN-based model, VTG-NET (vascular topology network), which can integrate vascular topology information into retinal A/V classification and outperforms state-of-the-art methods. CNN is currently a vital research direction based on DL in computer vision, which performs well in image classification, segmentation, and object detection.…”

Section: The Development Of Artificial Intelligence (Ai) and Its Adva...mentioning

confidence: 99%

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Chen

et al. 2023

Bio-des. Manuf.

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In modern terminology, “organoids” refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions. Graphic abstract

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“…Furthermore, some researchers perform the A/V classification tasks in the retina fundus by introducing the structural information into the pure convolution to alleviate the loss of the information and enlarge the receptive field, called the hybrid method. Mishra et al [ 14 ] proposed a novel method, which transforms the vessel feature from the image domain into a graph representation, utilizing the vessel topology to enhance the artery/vein classification. In [ 15 ], the retina vessel skeleton, which represents the direction of vessels, is extracted from different color spaces.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Segmentation and Classification Network for Artery/Vein Classification in Retina Fundus

Yi,

Chen

2023

Entropy

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Automatic classification of arteries and veins (A/V) in fundus images has gained considerable attention from researchers due to its potential to detect vascular abnormalities and facilitate the diagnosis of some systemic diseases. However, the variability in vessel structures and the marginal distinction between arteries and veins poses challenges to accurate A/V classification. This paper proposes a novel Multi-task Segmentation and Classification Network (MSC-Net) that utilizes the vessel features extracted by a specific module to improve A/V classification and alleviate the aforementioned limitations. The proposed method introduces three modules to enhance the performance of A/V classification: a Multi-scale Vessel Extraction (MVE) module, which distinguishes between vessel pixels and background using semantics of vessels, a Multi-structure A/V Extraction (MAE) module that classifies arteries and veins by combining the original image with the vessel features produced by the MVE module, and a Multi-source Feature Integration (MFI) module that merges the outputs from the former two modules to obtain the final A/V classification results. Extensive empirical experiments verify the high performance of the proposed MSC-Net for retinal A/V classification over state-of-the-art methods on several public datasets.

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VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification

Cited by 13 publications

References 24 publications

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Multi-Task Segmentation and Classification Network for Artery/Vein Classification in Retina Fundus

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