FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4226057713> ?p ?o ?g. }

• W4226057713 abstract "The digital pathology images obtain the essential information about the patient's disease, and the automated nuclei segmentation results can help doctors make better decisions about diagnosing the disease. With the speedy advancement of convolutional neural networks in image processing, deep learning has been shown to play a significant role in the various analysis of medical images, such as nuclei segmentation, mitosis detection and segmentation etc. Recently, several U-net based methods have been developed to solve the automated nuclei segmentation problems. However, these methods fail to deal with the weak features representation from the initial layers and introduce the noise into the decoder path. In this paper, we propose a multiscale attention learning network (MSAL-Net), where the dense dilated convolutions block captures more comprehensive nuclei context information, and a newly modified decoder part is introduced, which integrates with efficient channel attention and boundary refinement modules to effectively learn spatial information for better prediction and further refine the nuclei cell of boundaries.Both qualitative and quantitative results are obtained on the publicly available MoNuseg dataset. Extensive experiment results verify that our proposed method significantly outperforms state-of-the-art methods as well as the vanilla Unet method in the segmentation task. Furthermore, we visually demonstrate the effect of our modified decoder part.The MSAL-Net shows superiority with a novel decoder to segment the touching and blurred background nuclei cells obtained from histopathology images with better performance for accurate decoding." @default.
• W4226057713 created "2022-05-05" @default.
• W4226057713 creator A5002937692 @default.
• W4226057713 creator A5026963407 @default.
• W4226057713 creator A5039643101 @default.
• W4226057713 creator A5054461540 @default.
• W4226057713 date "2022-04-04" @default.
• W4226057713 modified "2023-09-26" @default.
• W4226057713 title "MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network" @default.
• W4226057713 cites W1901129140 @default.
• W4226057713 cites W1903029394 @default.
• W4226057713 cites W1970120446 @default.
• W4226057713 cites W2005814255 @default.
• W4226057713 cites W2143251344 @default.
• W4226057713 cites W2159551006 @default.
• W4226057713 cites W2161381512 @default.
• W4226057713 cites W2194775991 @default.
• W4226057713 cites W2412782625 @default.
• W4226057713 cites W2556697445 @default.
• W4226057713 cites W2567946946 @default.
• W4226057713 cites W2575678364 @default.
• W4226057713 cites W2590478046 @default.
• W4226057713 cites W2592905743 @default.
• W4226057713 cites W2598666589 @default.
• W4226057713 cites W2604440528 @default.
• W4226057713 cites W2615944727 @default.
• W4226057713 cites W2772030296 @default.
• W4226057713 cites W2883235649 @default.
• W4226057713 cites W2884436604 @default.
• W4226057713 cites W2885343725 @default.
• W4226057713 cites W2892938835 @default.
• W4226057713 cites W2894802018 @default.
• W4226057713 cites W2914010220 @default.
• W4226057713 cites W2955805844 @default.
• W4226057713 cites W2962949934 @default.
• W4226057713 cites W2964350391 @default.
• W4226057713 cites W2971403019 @default.
• W4226057713 cites W2979688198 @default.
• W4226057713 cites W2998642388 @default.
• W4226057713 cites W3012093480 @default.
• W4226057713 cites W3014785587 @default.
• W4226057713 cites W3034552520 @default.
• W4226057713 cites W3092052974 @default.
• W4226057713 cites W3094071141 @default.
• W4226057713 cites W3096502673 @default.
• W4226057713 cites W3103010481 @default.
• W4226057713 cites W3105636206 @default.
• W4226057713 cites W3161081823 @default.
• W4226057713 cites W3168341999 @default.
• W4226057713 cites W3210404356 @default.
• W4226057713 doi "https://doi.org/10.1186/s12911-022-01826-5" @default.
• W4226057713 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35379228" @default.
• W4226057713 hasPublicationYear "2022" @default.
• W4226057713 type Work @default.
• W4226057713 citedByCount "2" @default.
• W4226057713 countsByYear W42260577132022 @default.
• W4226057713 countsByYear W42260577132023 @default.
• W4226057713 crossrefType "journal-article" @default.
• W4226057713 hasAuthorship W4226057713A5002937692 @default.
• W4226057713 hasAuthorship W4226057713A5026963407 @default.
• W4226057713 hasAuthorship W4226057713A5039643101 @default.
• W4226057713 hasAuthorship W4226057713A5054461540 @default.
• W4226057713 hasBestOaLocation W42260577131 @default.
• W4226057713 hasConcept C108583219 @default.
• W4226057713 hasConcept C11413529 @default.
• W4226057713 hasConcept C115961682 @default.
• W4226057713 hasConcept C124504099 @default.
• W4226057713 hasConcept C151730666 @default.
• W4226057713 hasConcept C153180895 @default.
• W4226057713 hasConcept C154945302 @default.
• W4226057713 hasConcept C17744445 @default.
• W4226057713 hasConcept C199539241 @default.
• W4226057713 hasConcept C2776359362 @default.
• W4226057713 hasConcept C2777522853 @default.
• W4226057713 hasConcept C2779343474 @default.
• W4226057713 hasConcept C31972630 @default.
• W4226057713 hasConcept C41008148 @default.
• W4226057713 hasConcept C57273362 @default.
• W4226057713 hasConcept C65885262 @default.
• W4226057713 hasConcept C81363708 @default.
• W4226057713 hasConcept C86803240 @default.
• W4226057713 hasConcept C89600930 @default.
• W4226057713 hasConcept C94625758 @default.
• W4226057713 hasConcept C99498987 @default.
• W4226057713 hasConceptScore W4226057713C108583219 @default.
• W4226057713 hasConceptScore W4226057713C11413529 @default.
• W4226057713 hasConceptScore W4226057713C115961682 @default.
• W4226057713 hasConceptScore W4226057713C124504099 @default.
• W4226057713 hasConceptScore W4226057713C151730666 @default.
• W4226057713 hasConceptScore W4226057713C153180895 @default.
• W4226057713 hasConceptScore W4226057713C154945302 @default.
• W4226057713 hasConceptScore W4226057713C17744445 @default.
• W4226057713 hasConceptScore W4226057713C199539241 @default.
• W4226057713 hasConceptScore W4226057713C2776359362 @default.
• W4226057713 hasConceptScore W4226057713C2777522853 @default.
• W4226057713 hasConceptScore W4226057713C2779343474 @default.
• W4226057713 hasConceptScore W4226057713C31972630 @default.
• W4226057713 hasConceptScore W4226057713C41008148 @default.
• W4226057713 hasConceptScore W4226057713C57273362 @default.
• W4226057713 hasConceptScore W4226057713C65885262 @default.

• next