Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

Santini, Gianmarco; Moreau, Noémie; Rubeaux, Mathieu

doi:10.24926/548719.023

Cited by 20 publications

(6 citation statements)

References 28 publications

(36 reference statements)

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“…Those approaches begin by detecting the volume of interest (VOIs) and then segmenting the target organs from the VOIs (VOIs) [ 57 ]. Regarding two-stage methods [ 12 , 41 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ], Cruz et al [ 58 ] developed a method that uses deep convolutional neural networks with image processing techniques to delimit the kidneys in CT images, where they achieved up to 93.03% accuracy, so further improvements are required. Zhang et al [ 59 ] studied a cascaded two-stage framework using a 3D fully convolutional network (FCN) for kidney and tumor segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…They combined deep supervision with exponential and logarithmic loss to improve the efficiency of 3D U-Net training. Santini et al [ 62 ] combined Res-Net with Res-U-Net architectures in a multi-stage DL approach called EMS-DLA that has been used for kidney tumor segmentation. The results are promising, and they might be improved if an enhanced understanding of benign cysts is factored in.…”

Section: Related Workmentioning

confidence: 99%

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Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Abdelrahman

Viriri

2022

J. Imaging

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Cure rates for kidney cancer vary according to stage and grade; hence, accurate diagnostic procedures for early detection and diagnosis are crucial. Some difficulties with manual segmentation have necessitated the use of deep learning models to assist clinicians in effectively recognizing and segmenting tumors. Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. Simultaneously, researchers in the field of medical image segmentation employ DL approaches to solve problems such as tumor segmentation, cell segmentation, and organ segmentation. Segmentation of tumors semantically is critical in radiation and therapeutic practice. This article discusses current advances in kidney tumor segmentation systems based on DL. We discuss the various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation, highlighting their building blocks and various strategies.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Abdelrahman

Viriri

2022

J. Imaging

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“…Santini et al 29 presented an ensemble multistage DL model (EMS‐DLA) to segment the kidney tumors efficiently. An integration procedure would be used between individual methods to correlate the predicted outcome from the preceding stage.…”

Section: Related Workmentioning

confidence: 99%

Ebola deep wavelet extreme learning machine based chronic kidney disease prediction on the internet of medical things platform

Reddy

Vydeki

2022

Concurrency and Computation

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Summary Various significant methodologies have been developed in classifying chronic kidney disease (CKD). But still, there emerge certain drawbacks, including high storage space requirements, increased diagnosis time, high cost of computation, and degraded accuracy. Hence in the proposed research work, Ebola deep wavelet extreme learning machine (EDWELM) is proposed for the precise classification of CKD and non‐CKD. Initially, the accessed data are preprocessed by transforming categorical to numerical values and replacing missing values with median to remove unwanted distortions. In the feature selection process, the hybrid methodology of Darts game and Battle royale optimization called Darts Battle game optimizer is carried out to choose the most discriminative features for improving classification accuracy. The final step undertaken is the classification process, which is one of the important data mining applications for distinguishing the data classes. In the proposed EDWELM classification method, ELM based autoencoder, wavelet neural network, and Ebola optimization search algorithm are carried out for effective CKD classification. From the CKD dataset, the performance is analyzed to various terms like accuracy, F1 score, precision, recall, kappa, and balanced score. The accuracy of 99.83% is attained by the proposed EDWELM classification method, which is comparatively better than the existing approaches.

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“…Santini et al [25] introduced the Ensembling Multi-stage deep learning approach (EMS-DLA) for kidney tumor segmentation. To integrate prediction results from previous phases, a combining procedure will be applied to the variance between individual models.…”

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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Kidney tumor segmentation using an ensembling multi-stage deep learning approach. A contribution to the KiTS19 challenge

Cited by 20 publications

References 28 publications

Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art

Ebola deep wavelet extreme learning machine based chronic kidney disease prediction 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

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