ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization
Renal tumor malignancy classification is one of the crucial tasks in urology, being a primary factor included in the decision of whether to perform kidney removal surgery (nephrectomy) or not. Currently, tumor malignancy prediction is determined by the radiological diagnosis based on computed tomogr...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2022-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/130689 |
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| author | Aleksander Obuchowski Barbara Klaudel Roman Karski Bartosz Rydziński Mateusz Glembin Paweł Syty Patryk Jasik |
| author_facet | Aleksander Obuchowski Barbara Klaudel Roman Karski Bartosz Rydziński Mateusz Glembin Paweł Syty Patryk Jasik |
| author_sort | Aleksander Obuchowski |
| collection | DOAJ |
| description | Renal tumor malignancy classification is one of the crucial tasks in urology, being a primary factor included in the decision of whether to perform kidney removal surgery (nephrectomy) or not. Currently, tumor malignancy prediction is determined by the radiological diagnosis based on computed tomography (CT) images. However, it is estimated that up to 16% of nephrectomies could have been avoided because the tumor that had been diagnosed as malignant, was found to be benign in the postoperative histopathological examination. The excess of false-positive diagnoses results in unnecessarily performed nephrectomies that carry the risk of periprocedural complications. In this paper, we present a machine-aided diagnosis system that predicts the tumor malignancy based on a CT image. The prediction is performed after radiological diagnosis and is used to capture false-positive diagnoses. Our solution is able to achieve a 0.84 F1-score in this task. We also propose a novel approach to knowledge transfer in the medical domain in terms of colorization based pre-processing that is able to increase the F1-score by up to 1.8pp. |
| format | Article |
| id | doaj-art-5b150cd99d4e44c7a7d1688dd57cc986 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-5b150cd99d4e44c7a7d1688dd57cc9862025-08-20T03:05:26ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622022-05-013510.32473/flairs.v35i.13068966888ColorNephroNet: Kidney tumor malignancy prediction using medical image colorizationAleksander Obuchowski0Barbara Klaudel1Roman Karski2Bartosz Rydziński3Mateusz Glembin4Paweł SytyPatryk Jasik5Gdańsk University of TechnologyGdańsk University of TechnologyGdańsk University of TechnologyGdańsk University of TechnologyCOPERNICUS, St. Adalbert’s Hospital Gda ́nsk, Department of UrologyGdańsk University of TechnologyRenal tumor malignancy classification is one of the crucial tasks in urology, being a primary factor included in the decision of whether to perform kidney removal surgery (nephrectomy) or not. Currently, tumor malignancy prediction is determined by the radiological diagnosis based on computed tomography (CT) images. However, it is estimated that up to 16% of nephrectomies could have been avoided because the tumor that had been diagnosed as malignant, was found to be benign in the postoperative histopathological examination. The excess of false-positive diagnoses results in unnecessarily performed nephrectomies that carry the risk of periprocedural complications. In this paper, we present a machine-aided diagnosis system that predicts the tumor malignancy based on a CT image. The prediction is performed after radiological diagnosis and is used to capture false-positive diagnoses. Our solution is able to achieve a 0.84 F1-score in this task. We also propose a novel approach to knowledge transfer in the medical domain in terms of colorization based pre-processing that is able to increase the F1-score by up to 1.8pp.https://journals.flvc.org/FLAIRS/article/view/130689medical image classificationtransfer learningcolorizationrenaltumorcomputer aided diagnosis |
| spellingShingle | Aleksander Obuchowski Barbara Klaudel Roman Karski Bartosz Rydziński Mateusz Glembin Paweł Syty Patryk Jasik ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization Proceedings of the International Florida Artificial Intelligence Research Society Conference medical image classification transfer learning colorization renal tumor computer aided diagnosis |
| title | ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization |
| title_full | ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization |
| title_fullStr | ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization |
| title_full_unstemmed | ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization |
| title_short | ColorNephroNet: Kidney tumor malignancy prediction using medical image colorization |
| title_sort | colornephronet kidney tumor malignancy prediction using medical image colorization |
| topic | medical image classification transfer learning colorization renal tumor computer aided diagnosis |
| url | https://journals.flvc.org/FLAIRS/article/view/130689 |
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