Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging

Yin, Shan; Peng, Qinmu; Li, Hongming; Zhang, Zhengqiang; You, Xinge; Liu, Hangfan; Fischer, Katherine M.; Furth, Susan L.; Tasian, Gregory E.; Fan, Yong

doi:10.1007/978-3-030-32689-0_15

Cited by 26 publications

(19 citation statements)

References 9 publications

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“…Multiple instance learning has been used to estimate instance-level classification probabilities and fuse them to generate a bag-level classification probability, but correlation between instances has not been well explored. To improve these methods, Yin et al [ 125 ] introduced a graph-based methodology to detect children with congenital anomalies of the kidneys and urinary tract in 2D US images. A CNN is used to learn informative US image features at the instance level and a GCN is used as a permutation-invariant operator to further optimize the instance-level CNN features by exploring potential correlations among different instances of the same bag.…”

Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

133

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

133

View full text Add to dashboard Cite

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“…Machine learning and deep learning models have been successfully applied in the context of HN to predict the need for surgical intervention (1), and the necessity of diuretic nuclear renography (2). More broadly, machine learning and deep learning have been used in the field of pediatric urology to classify between different kidney diseases (23), and between diseased and normal kidneys (24). In addition, deep learning has recently been used to perform automatic kidney segmentation in ultrasound imaging (25).…”

Section: Implications For Clinical Practicementioning

confidence: 99%

Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct

et al. 2020

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Grading hydronephrosis severity relies on subjective interpretation of renal ultrasound images. Deep learning is a data-driven algorithmic approach to classifying data, including images, presenting a promising option for grading hydronephrosis. The current study explored the potential of deep convolutional neural networks (CNN), a type of deep learning algorithm, to grade hydronephrosis ultrasound images according to the 5-point Society for Fetal Urology (SFU) classification system, and discusses its potential applications in developing decision and teaching aids for clinical practice. We developed a five-layer CNN to grade 2,420 sagittal hydronephrosis ultrasound images [191 SFU 0 (8%), 407 SFU I (17%), 666 SFU II (28%), 833 SFU III (34%), and 323 SFU IV (13%)], from 673 patients ranging from 0 to 116.29 months old (M age = 16.53, SD = 17.80). Five-way (all grades) and two-way classification problems [i.e., II vs. III, and low (0-II) vs. high (III-IV)] were explored. The CNN classified 94% (95% CI, 93-95%) of the images correctly or within one grade of the provided label in the five-way classification problem. Fifty-one percent of these images (95% CI, 49-53%) were correctly predicted, with an average weighted F1 score of 0.49 (95% CI, 0.47-0.51). The CNN achieved an average accuracy of 78% (95% CI, 75-82%) with an average weighted F1 of 0.78 (95% CI, 0.74-0.82) when classifying low vs. high grades, and an average accuracy of 71% (95% CI, 68-74%) with an average weighted F1 score of 0.71 (95% CI, 0.68-0.75) when discriminating between grades II vs. III. Our model performs well above chance level, and classifies almost all images either correctly or within one grade of the provided label. We have demonstrated the applicability of a CNN approach to hydronephrosis ultrasound image classification. Further investigation into a deep learning-based clinical adjunct for hydronephrosis is warranted.

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“…ResNet18 and GoogLeNet models were used by Storey et al to detect wrist bone abnormality [21]. As a result of the classification made by Yin et al for kidney disease with the multi-instance deep learning method, an accuracy of 88.6% was obtained [22]. As a result of the detection of musculoskeletal abnormalities performed by Dias, the highest accuracy was obtained as 81.98% with the SqueezeNet model [23].…”

Section: Classification Studies Carried Out On the Shoulder Bonementioning

confidence: 99%

Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

et al. 2021

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Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

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Multi-instance Deep Learning with Graph Convolutional Neural Networks for Diagnosis of Kidney Diseases Using Ultrasound Imaging

Cited by 26 publications

References 9 publications

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Using Deep Learning Algorithms to Grade Hydronephrosis Severity: Toward a Clinical Adjunct

Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

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