Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks

Sheehan, Susan; Mawe, Seamus; Cianciolo, Rachel E.; Korstanje, Ron; Mahoney, James

doi:10.1016/j.ajpath.2019.05.019

Cited by 29 publications

(19 citation statements)

References 17 publications

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“…This sub-set of miRNAs allows LSTM to group kidney cancer miRNAs into five sub-types with an average accuracy of about 95% and Matthews's correlation coefficient values of about 0.92 under 10 random clustered 5 times, which are very close to the average output of all miRNAs for rating purposes. Sheehan et al [22] introduced the Deep Neural Network (DNN) for the detection and classification of novel renal histologic phenotypes. They demonstrate that machine learning with DNN has strong widespread performance in several processing tasks of histologic images.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Chronic Kidney Disease Using Adaptive Hybridized Deep Convolutional Neural Network on the Internet of Medical Things Platform

Chen

Ding

et al. 2020

IEEE Access

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Chronic Kidney disease is a severe lifelong condition caused either by renal disease or by impaired functions of the kidneys. In the present area of research, Kidney cancer is one of the deadliest and crucial importance for the survival of the patients ' diagnosis and classification. Early diagnosis and proper therapy can stop or delay the development of this chronic disease into the final stage where dialysis or renal transplantation is the only way of saving the life of the patient. The development of automated tools to accurately identify subtypes of kidney cancer is, therefore, an urgent challenge in the recent past. In this paper, to examine the ability of various deep learning methods an Adaptive hybridized Deep Convolutional Neural Network (AHDCNN) has been proposed for the early detection of Kidney disease efficiently and effectively. Classification technology efficiency depends on the role of the data set. To enhance the accuracy of the classification system by reducing the feature dimension an algorithm model has been developed using CNN. These high-level properties help to build a supervised tissue classifier that discriminates between the two types of tissue. The experimental process on the Internet of medical things platform (IoMT)concludes, with the aid of predictive analytics, that advances in machine learning which provides a promising framework for the recognition of intelligent solutions to prove their predictive capability beyond the field of kidney disease. INDEX TERMS Chronic kidney disease, deep learning, convolutional neural network, IoMT.

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Section: Related Workmentioning

confidence: 99%

Prediction of Chronic Kidney Disease Using Adaptive Hybridized Deep Convolutional Neural Network on the Internet of Medical Things Platform

Chen

Ding

et al. 2020

IEEE Access

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“…Furthermore, it was proven that the usage of the ML algorithm can not only clearly segment the glomeruli from the kidney image but also distinguish the renal tubule. Sheehan et al [ 34 ] used SVM classification to extract the features of renal tubules in mice (true positive rate, 92%; false-positive rate, 10%). Using 200 cores on the Vermont Advanced Compute Cluster, the glomerular segmentation pipeline can segment the full-sized mouse kidney section in approximately 40 min, allowing analysis of more glomeruli than manual completion.…”

Section: In Nephrologymentioning

confidence: 99%

“…The sample size of the study is generally small, and the pictures used for modeling are basically from pathological sections of the kidneys of mice. [ 32 , 34 ] Although AI has developed rapidly, due to the complexity of pathological manifestations of various renal diseases and their close relationship with clinical indicators, the automated pathological diagnosis of specific renal diseases based on images has not yet been published. It is not possible to completely replace pathologists for the renal pathological diagnosis of all kinds of kidney diseases through comprehensive patient information.…”

Section: Challenges and Future Prospectsmentioning

confidence: 99%

Machine learning in nephrology: scratching the surface

Fan

Han

et al. 2020

Chinese Medical Journal

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Machine learning shows enormous potential in facilitating decision-making regarding kidney diseases. With the development of data preservation and processing, as well as the advancement of machine learning algorithms, machine learning is expected to make remarkable breakthroughs in nephrology. Machine learning models have yielded many preliminaries to moderate and several excellent achievements in the fields, including analysis of renal pathological images, diagnosis and prognosis of chronic kidney diseases and acute kidney injury, as well as management of dialysis treatments. However, it is just scratching the surface of the field; at the same time, machine learning and its applications in renal diseases are facing a number of challenges. In this review, we discuss the application status, challenges and future prospects of machine learning in nephrology to help people further understand and improve the capacity for prediction, detection, and care quality in kidney diseases.

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“…QIFs were extracted from the intermediate 'fully connected layer 6' from AlexNet, which outputs 4096 QIFs. We recently showed that these 4096 features work well for transfer learning in histopathological studies of kidney disease [20]. AlexNet has a fixed input size of 227 × 227 pixels (~0.16 mm 2 ), thus randomly sampled 227 × 227-pixel image patches from the dermis of each section were transformed into QIFs.…”

Section: Deep Neural Network Feature Extractionmentioning

confidence: 99%

High-throughput quantitative histology in systemic sclerosis skin disease using computer vision

et al. 2020

Self Cite

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Background: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time. The most widely used outcome measure in SSc clinical trials is the modified Rodnan skin score (mRSS), which is a semi-quantitative assessment of skin stiffness at seventeen body sites. However, the mRSS is confounded by obesity, edema, and high inter-rater variability. In order to develop a new histopathological outcome measure for SSc, we applied a computer vision technology called a deep neural network (DNN) to stained sections of SSc skin. We tested the hypotheses that DNN analysis could reliably assess mRSS and discriminate SSc from normal skin. Methods: We analyzed biopsies from two independent (primary and secondary) cohorts. One investigator performed mRSS assessments and forearm biopsies, and trichrome-stained biopsy sections were photomicrographed. We used the AlexNet DNN to generate a numerical signature of 4096 quantitative image features (QIFs) for 100 randomly selected dermal image patches/biopsy. In the primary cohort, we used principal components analysis (PCA) to summarize the QIFs into a Biopsy Score for comparison with mRSS. In the secondary cohort, using QIF signatures as the input, we fit a logistic regression model to discriminate between SSc vs. control biopsy, and a linear regression model to estimate mRSS, yielding Diagnostic Scores and Fibrosis Scores, respectively. We determined the correlation between Fibrosis Scores and the published Scleroderma Skin Severity Score (4S) and between Fibrosis Scores and longitudinal changes in mRSS on a per patient basis. Results: In the primary cohort (n = 6, 26 SSc biopsies), Biopsy Scores significantly correlated with mRSS (R = 0.55, p = 0.01). In the secondary cohort (n = 60 SSc and 16 controls, 164 biopsies; divided into 70% training and 30% test sets), the Diagnostic Score was significantly associated with SSc-status (misclassification rate = 1.9% [training], 6.6% [test]), and the Fibrosis Score significantly correlated with mRSS (R = 0.70 [training], 0.55 [test]). The DNN-derived Fibrosis Score significantly correlated with 4S (R = 0.69, p = 3 × 10 − 17). Conclusions: DNN analysis of SSc biopsies is an unbiased, quantitative, and reproducible outcome that is associated with validated SSc outcomes.

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Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks

Cited by 29 publications

References 17 publications

Prediction of Chronic Kidney Disease Using Adaptive Hybridized Deep Convolutional Neural Network on the Internet of Medical Things Platform

Prediction of Chronic Kidney Disease Using Adaptive Hybridized Deep Convolutional Neural Network on the Internet of Medical Things Platform

Machine learning in nephrology: scratching the surface

High-throughput quantitative histology in systemic sclerosis skin disease using computer vision

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