CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma

Lin, Fan; Cui, Enming; Yi, Lei; Luo, Lixia

doi:10.1007/s00261-019-01992-7

Cited by 59 publications

(58 citation statements)

References 35 publications

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“…We find one study related to clear cell renal cell carcinoma ( ), “Ct-based machine learning model to predict the fuhrman nuclear grade of clear cell renal cell carcinoma” [ 28 ], by Lin et al According to The National Cancer Institute [ 103 ], is a form of kidney cancer that makes up about 80% of all kidney cancer cases. Therefore, advances in treating stand to benefit the largest number of people who suffer from kidney cancer.…”

Section: Catboost Applications By Fieldmentioning

confidence: 99%

“…Researchers in disparate domains find applications for CatBoost. We find works in the fields of Astronomy [ 18 ], Finance [ 19 – 22 ], Medicine [ 23 – 26 ], Biology [ 27 , 28 ], Electrical Utilities Fraud [ 29 – 31 ], Meteorology [ 32 , 33 ], Psychology [ 34 , 35 ], Traffic Engineering [ 7 , 36 ], Cyber-security [ 37 ], Bio-chemistry [ 5 , 38 ], and Marketing [ 39 ]. Therefore, a good understanding of CatBoost may provide one the opportunity to participate in interdisciplinary research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

CatBoost for big data: an interdisciplinary review

2020

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Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

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Section: Catboost Applications By Fieldmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CatBoost for big data: an interdisciplinary review

2020

View full text Add to dashboard Cite

show abstract

“…We find one study related to clear cell renal cell carcinoma (ccRCC), "Ct-based machine learning model to predict the fuhrman nuclear grade of clear cell renal cell carcinoma" [55], by Lin et al According to The National Cancer Institute [45], ccRCC is a form of kidney cancer that makes up about 80% of all kidney cancer cases. Therefore, advances in treating ccRCC stand to benefit the largest number of people who suffer from kidney cancer.…”

Section: Biologymentioning

confidence: 99%

“…Researchers in disparate domains find applications for CatBoost. We find works in the fields of Astronomy [49], Finance [24] [92] [91] [89], Medicine [87] [52] [4] [68], Biology [51] [55], Electrical Utilities Fraud [20] [66] [36], Meteorology [44] [29], Psychology [71] [3], Traffic Engineering [85] [76], Cyber-security [6], Bio-chemistry [88] [58], and Marketing [50]. Therefore, a good understanding of CatBoost may provide one the opportunity to participate in interdisciplinary research.…”

Section: Introductionmentioning

confidence: 99%

CatBoost for Big Data: an Interdisciplinary Review

Hancock

Khoshgoftaar

2020

Preprint

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Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data, Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have ellCcessfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that .55 CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

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“…Image from[60] illustrating best results for ensemble of CatBoost and GRU; Here, the authors refer to CatBoost as Gradient Boosted Machine (GBM) Image from[96] illustrating neural network based algorithms outperforming Gradient Boosted tree algorithms for classifying homogeneous text data in the SoHu dataset of news articles labeled as with or without marketing intentBiologyWe find one study related to clear cell renal cell carcinoma (ccRCC), "Ct-based machine learning model to predict the fuhrman nuclear grade of clear cell renal cell carcinoma"[66], by Lin et al According to The National Cancer Institute [53],…”

mentioning

confidence: 99%

CatBoost for Big Data: an Interdisciplinary Review

Hancock

Khoshgoftaar

2020

Preprint

View full text Add to dashboard Cite

Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classiﬁcation and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them eﬀectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s eﬀectiveness and shortcomings in classiﬁcation and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the ﬁrst survey that studies all works related to CatBoost in a single publication.

show abstract

CT-based machine learning model to predict the Fuhrman nuclear grade of clear cell renal cell carcinoma

Cited by 59 publications

References 35 publications

CatBoost for big data: an interdisciplinary review

CatBoost for big data: an interdisciplinary review

CatBoost for Big Data: an Interdisciplinary Review

CatBoost for Big Data: an Interdisciplinary Review

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