FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4293685816> ?p ?o ?g. }

• W4293685816 endingPage "423" @default.
• W4293685816 startingPage "423" @default.
• W4293685816 abstract "Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identification of RCC is time-consuming and prone to inter-subject variability. In this paper, we aim to distinguish between benign tissue and malignant RCC tumors and identify the tumor subtypes to support medical therapy management. We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping. Our system starts by applying the RGB-histogram specification stain normalization on the whole slide images to eliminate the effect of the color variations on the system performance. Then, we follow the multiple instance learning approach by dividing the input data into multiple overlapping patches to maintain the tissue connectivity. Finally, we train three multiscale convolutional neural networks (CNNs) and apply decision fusion to their predicted results to obtain the final classification decision. Our dataset comprises four classes of renal tissues: non-RCC renal parenchyma, non-RCC fat tissues, clear cell RCC (ccRCC), and clear cell papillary RCC (ccpRCC). The developed system demonstrates a high classification accuracy and sensitivity on the RCC biopsy samples at the slide level. Following a leave-one-subject-out cross-validation approach, the developed RCC subtype classification system achieves an overall classification accuracy of 93.0% ± 4.9%, a sensitivity of 91.3% ± 10.7%, and a high classification specificity of 95.6% ± 5.2%, in distinguishing ccRCC from ccpRCC or non-RCC tissues. Furthermore, our method outperformed the state-of-the-art Resnet-50 model." @default.
• W4293685816 created "2022-08-31" @default.
• W4293685816 creator A5020634024 @default.
• W4293685816 creator A5040179322 @default.
• W4293685816 creator A5050163041 @default.
• W4293685816 creator A5075057705 @default.
• W4293685816 date "2022-08-30" @default.
• W4293685816 modified "2023-10-01" @default.
• W4293685816 title "Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images" @default.
• W4293685816 cites W1173721425 @default.
• W4293685816 cites W2057114171 @default.
• W4293685816 cites W2129112648 @default.
• W4293685816 cites W2530608546 @default.
• W4293685816 cites W2796207353 @default.
• W4293685816 cites W2885685834 @default.
• W4293685816 cites W2886327600 @default.
• W4293685816 cites W2889646458 @default.
• W4293685816 cites W2894730897 @default.
• W4293685816 cites W2949342504 @default.
• W4293685816 cites W2964345665 @default.
• W4293685816 cites W2975662018 @default.
• W4293685816 cites W3007748798 @default.
• W4293685816 cites W3033015518 @default.
• W4293685816 cites W3075287024 @default.
• W4293685816 cites W3089090082 @default.
• W4293685816 cites W3091946902 @default.
• W4293685816 cites W3094977690 @default.
• W4293685816 cites W3101198680 @default.
• W4293685816 cites W3120795911 @default.
• W4293685816 cites W3125402839 @default.
• W4293685816 cites W3126827997 @default.
• W4293685816 cites W3132591370 @default.
• W4293685816 cites W3135530772 @default.
• W4293685816 cites W3135547872 @default.
• W4293685816 cites W3135627022 @default.
• W4293685816 cites W3167269280 @default.
• W4293685816 cites W3174477388 @default.
• W4293685816 cites W3182999054 @default.
• W4293685816 cites W3185939190 @default.
• W4293685816 cites W3208624098 @default.
• W4293685816 cites W4200119336 @default.
• W4293685816 cites W4225597912 @default.
• W4293685816 cites W4294358748 @default.
• W4293685816 doi "https://doi.org/10.3390/bioengineering9090423" @default.
• W4293685816 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36134972" @default.
• W4293685816 hasPublicationYear "2022" @default.
• W4293685816 type Work @default.
• W4293685816 citedByCount "4" @default.
• W4293685816 countsByYear W42936858162023 @default.
• W4293685816 crossrefType "journal-article" @default.
• W4293685816 hasAuthorship W4293685816A5020634024 @default.
• W4293685816 hasAuthorship W4293685816A5040179322 @default.
• W4293685816 hasAuthorship W4293685816A5050163041 @default.
• W4293685816 hasAuthorship W4293685816A5075057705 @default.
• W4293685816 hasBestOaLocation W42936858161 @default.
• W4293685816 hasConcept C142724271 @default.
• W4293685816 hasConcept C153180895 @default.
• W4293685816 hasConcept C154945302 @default.
• W4293685816 hasConcept C199360897 @default.
• W4293685816 hasConcept C2777472916 @default.
• W4293685816 hasConcept C41008148 @default.
• W4293685816 hasConcept C71924100 @default.
• W4293685816 hasConcept C83852419 @default.
• W4293685816 hasConceptScore W4293685816C142724271 @default.
• W4293685816 hasConceptScore W4293685816C153180895 @default.
• W4293685816 hasConceptScore W4293685816C154945302 @default.
• W4293685816 hasConceptScore W4293685816C199360897 @default.
• W4293685816 hasConceptScore W4293685816C2777472916 @default.
• W4293685816 hasConceptScore W4293685816C41008148 @default.
• W4293685816 hasConceptScore W4293685816C71924100 @default.
• W4293685816 hasConceptScore W4293685816C83852419 @default.
• W4293685816 hasIssue "9" @default.
• W4293685816 hasLocation W42936858161 @default.
• W4293685816 hasLocation W42936858162 @default.
• W4293685816 hasLocation W42936858163 @default.
• W4293685816 hasOpenAccess W4293685816 @default.
• W4293685816 hasPrimaryLocation W42936858161 @default.
• W4293685816 hasRelatedWork W2005149537 @default.
• W4293685816 hasRelatedWork W2033914206 @default.
• W4293685816 hasRelatedWork W2146076056 @default.
• W4293685816 hasRelatedWork W2163831990 @default.
• W4293685816 hasRelatedWork W2360810719 @default.
• W4293685816 hasRelatedWork W2748952813 @default.
• W4293685816 hasRelatedWork W2790557737 @default.
• W4293685816 hasRelatedWork W2899084033 @default.
• W4293685816 hasRelatedWork W3003836766 @default.
• W4293685816 hasRelatedWork W4384944765 @default.
• W4293685816 hasVolume "9" @default.
• W4293685816 isParatext "false" @default.
• W4293685816 isRetracted "false" @default.
• W4293685816 workType "article" @default.