{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:20:48Z","timestamp":1740108048154,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"29","license":[{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Islamic World Educational, Scientific and Cultural. Organization","award":["ICESCO Chair of Data Science and Analytics for Business"],"award-info":[{"award-number":["ICESCO Chair of Data Science and Analytics for Business"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s00521-024-10114-4","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T14:29:43Z","timestamp":1721917783000},"page":"18343-18361","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AFINITI: attention-aware feature integration for nuclei instance segmentation and type identification"],"prefix":"10.1007","volume":"36","author":[{"given":"Esha Sadia","family":"Nasir","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahzad","family":"Rasool","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raheel","family":"Nawaz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0495-463X","authenticated-orcid":false,"given":"Muhammad Moazam","family":"Fraz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"10114_CR1","doi-asserted-by":"publisher","first-page":"7909","DOI":"10.1007\/s10462-022-10372-5","volume":"56","author":"ES Nasir","year":"2023","unstructured":"Nasir ES, Parvaiz A, Fraz MM (2023) Nuclei and glands instance segmentation in histology images: a narrative review. Artif Intell Rev 56:7909\u20137964. https:\/\/doi.org\/10.1007\/s10462-022-10372-5","journal-title":"Artif Intell Rev"},{"key":"10114_CR2","doi-asserted-by":"publisher","first-page":"118945","DOI":"10.1016\/j.eswa.2022.118945","volume":"213","author":"I Ahmad","year":"2023","unstructured":"Ahmad I, Xia Y, Cui H, Islam ZU (2023) Dan-nucnet: a dual attention based framework for nuclei segmentation. Expert Syst Appl 213:118945","journal-title":"Expert Syst Appl"},{"key":"10114_CR3","doi-asserted-by":"publisher","first-page":"113387","DOI":"10.1016\/j.eswa.2020.113387","volume":"151","author":"BM Priego-Torres","year":"2020","unstructured":"Priego-Torres BM, Sanchez-Morillo D, Fernandez-Granero MA, Garcia-Rojo M (2020) Automatic segmentation of whole-slide h &e stained breast histopathology images using a deep convolutional neural network architecture. Expert Syst Appl 151:113387. https:\/\/doi.org\/10.1016\/j.eswa.2020.113387","journal-title":"Expert Syst Appl"},{"issue":"21","key":"10114_CR4","doi-asserted-by":"publisher","first-page":"15447","DOI":"10.1007\/s00521-023-08503-2","volume":"35","author":"SN Rashid","year":"2023","unstructured":"Rashid SN, Fraz MM (2023) Nuclei probability and centroid map network for nuclei instance segmentation in histology images. Neural Comput Appl 35(21):15447\u201315460. https:\/\/doi.org\/10.1007\/s00521-023-08503-2","journal-title":"Neural Comput Appl"},{"key":"10114_CR5","doi-asserted-by":"publisher","first-page":"101563","DOI":"10.1016\/j.media.2019.101563","volume":"58","author":"S Graham","year":"2019","unstructured":"Graham S, Vu QD, Raza SEA, Azam A, Tsang YW, Kwak JT, Rajpoot N (2019) Hover-net: simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med Image Anal 58:101563. https:\/\/doi.org\/10.1016\/j.media.2019.101563","journal-title":"Med Image Anal"},{"key":"10114_CR6","doi-asserted-by":"publisher","unstructured":"Bagdigen ME, Bilgin G (2020) Cell segmentation in triple-negative breast cancer histopathological images using u-net architecture. In: 2020 28th signal processing and communications applications conference (SIU), pp 1\u20134 . https:\/\/doi.org\/10.1109\/SIU49456.2020.9302367","DOI":"10.1109\/SIU49456.2020.9302367"},{"key":"10114_CR7","doi-asserted-by":"crossref","unstructured":"Rashid S, Fraz M, Javed S (2020) Multiscale dilated unet for segmentation of multi-organ nuclei in digital histology images. In: 2020 IEEE 17th international conference on smart communities: improving quality of life using ICT, IoT and AI (HONET), IEEE, pp 68\u201372","DOI":"10.1109\/HONET50430.2020.9322833"},{"key":"10114_CR8","first-page":"433738","volume":"53","author":"QD Vu","year":"2019","unstructured":"Vu QD, Graham S, Kurc T, To MNN, Shaban M, Qaiser T, Koohbanani NA, Khurram SA, Kalpathy-Cramer J, Zhao T et al (2019) Methods for segmentation and classification of digital microscopy tissue images. Front Bioeng Biotechnol 53:433738","journal-title":"Front Bioeng Biotechnol"},{"issue":"14","key":"10114_CR9","doi-asserted-by":"publisher","first-page":"9915","DOI":"10.1007\/s00521-019-04516-y","volume":"32","author":"MM Fraz","year":"2019","unstructured":"Fraz MM, Khurram SA, Graham S, Shaban M, Hassan M, Loya A, Rajpoot NM (2019) Fabnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer. Neural Comput Appl 32(14):9915\u20139928. https:\/\/doi.org\/10.1007\/s00521-019-04516-y","journal-title":"Neural Comput Appl"},{"issue":"1","key":"10114_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-49710-z","volume":"9","author":"M Shaban","year":"2019","unstructured":"Shaban M, Khurram SA, Fraz MM, Alsubaie N, Masood I, Mushtaq S, Hassan M, Loya A, Rajpoot NM (2019) A novel digital score for abundance of tumour infiltrating lymphocytes predicts disease free survival in oral squamous cell carcinoma. Sci Rep 9(1):1\u201313","journal-title":"Sci Rep"},{"key":"10114_CR11","doi-asserted-by":"crossref","unstructured":"Nawshad MA, Shami UA, Sajid S, Fraz MM (2021) Attention based residual network for effective detection of covid-19 and viral pneumonia. In: 2021 international conference on digital futures and transformative technologies (ICoDT2), IEEE, pp 1\u20137","DOI":"10.1109\/ICoDT252288.2021.9441485"},{"key":"10114_CR12","doi-asserted-by":"crossref","unstructured":"Bashir RS, Mahmood H, Shaban M, Raza SEA, Fraz MM, Khurram SA, Rajpoot NM (2020) Automated grade classification of oral epithelial dysplasia using morphometric analysis of histology images. In: Medical Imaging 2020: Digital Pathology, SPIE, vol. 11320, pp 245\u2013250","DOI":"10.1117\/12.2549705"},{"key":"10114_CR13","doi-asserted-by":"publisher","first-page":"119452","DOI":"10.1016\/j.eswa.2022.119452","volume":"216","author":"U Zidan","year":"2023","unstructured":"Zidan U, Gaber MM, Abdelsamea MM (2023) Swincup: Cascaded swin transformer for histopathological structures segmentation in colorectal cancer. Expert Syst Appl 216:119452. https:\/\/doi.org\/10.1016\/j.eswa.2022.119452","journal-title":"Expert Syst Appl"},{"issue":"16","key":"10114_CR14","doi-asserted-by":"publisher","first-page":"6521","DOI":"10.1016\/j.eswa.2013.06.010","volume":"40","author":"A LaTorre","year":"2013","unstructured":"LaTorre A, Alonso-Nanclares L, Muelas S, Pe\u00f1a JM, DeFelipe J (2013) Segmentation of neuronal nuclei based on clump splitting and a two-step binarization of images. Expert Syst Appl 40(16):6521\u20136530. https:\/\/doi.org\/10.1016\/j.eswa.2013.06.010","journal-title":"Expert Syst Appl"},{"issue":"10","key":"10114_CR15","doi-asserted-by":"publisher","first-page":"2600","DOI":"10.1109\/TBME.2010.2060336","volume":"57","author":"C Jung","year":"2010","unstructured":"Jung C, Kim C (2010) Segmenting clustered nuclei using h-minima transform-based marker extraction and contour parameterization. IEEE Trans Biomed Eng 57(10):2600\u20132604. https:\/\/doi.org\/10.1109\/TBME.2010.2060336","journal-title":"IEEE Trans Biomed Eng"},{"key":"10114_CR16","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. https:\/\/arxiv.org\/abs\/1703.06870","DOI":"10.1109\/ICCV.2017.322"},{"issue":"5","key":"10114_CR17","doi-asserted-by":"publisher","first-page":"1196","DOI":"10.1109\/TMI.2016.2525803","volume":"35","author":"K Sirinukunwattana","year":"2016","unstructured":"Sirinukunwattana K, Raza SEA, Tsang Y-W, Snead DRJ, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging 35(5):1196\u20131206. https:\/\/doi.org\/10.1109\/TMI.2016.2525803","journal-title":"IEEE Trans Med Imaging"},{"key":"10114_CR18","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. https:\/\/arxiv.org\/abs\/1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"8","key":"10114_CR19","doi-asserted-by":"publisher","first-page":"5839","DOI":"10.1007\/s00521-022-07966-z","volume":"35","author":"R Rashmi","year":"2022","unstructured":"Rashmi R, Prasad K, Udupa CBK (2022) Region-based feature enhancement using channel-wise attention for classification of breast histopathological images. Neural Comput Appl 35(8):5839\u20135854. https:\/\/doi.org\/10.1007\/s00521-022-07966-z","journal-title":"Neural Comput Appl"},{"key":"10114_CR20","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-32239-7_69","volume-title":"Medical image computing and computer assisted intervention - MICCAI 2019","author":"N Alemi Koohbanani","year":"2019","unstructured":"Alemi Koohbanani N, Jahanifar M, Gooya A, Rajpoot N (2019) Nuclear instance segmentation using a proposal-free spatially aware deep learning framework. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P-T, Khan A (eds) Medical image computing and computer assisted intervention - MICCAI 2019. Springer, Cham, pp 622\u2013630"},{"key":"10114_CR21","unstructured":"Shah S, Ghosh P, Davis LS, Goldstein T (2018) Stacked U-Nets: a no-frills approach to natural image segmentation. https:\/\/arxiv.org\/abs\/1804.10343"},{"key":"10114_CR22","doi-asserted-by":"crossref","unstructured":"Chen S, Ding C, Tao D (2020) Boundary-assisted region proposal networks for nucleus segmentation. https:\/\/arxiv.org\/abs\/2006.02695","DOI":"10.1007\/978-3-030-59722-1_27"},{"issue":"26","key":"10114_CR23","doi-asserted-by":"publisher","first-page":"19187","DOI":"10.1007\/s00521-023-08729-0","volume":"35","author":"A Sharma","year":"2023","unstructured":"Sharma A, Mishra PK (2023) Dri-unet: dense residual-inception unet for nuclei identification in microscopy cell images. Neural Comput Appl 35(26):19187\u201319220. https:\/\/doi.org\/10.1007\/s00521-023-08729-0","journal-title":"Neural Comput Appl"},{"key":"10114_CR24","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/978-3-030-00934-2_30","volume-title":"Medical image computing and computer assisted intervention - MICCAI 2018","author":"U Schmidt","year":"2018","unstructured":"Schmidt U, Weigert M, Broaddus C, Myers G (2018) Cell detection with star-convex polygons. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-L\u00f3pez C, Fichtinger G (eds) Medical image computing and computer assisted intervention - MICCAI 2018. Springer, Cham, pp 265\u2013273"},{"key":"10114_CR25","unstructured":"Chen S, Ding C, Liu M, Tao D (2021) CPP-Net: context-aware polygon proposal network for nucleus segmentation. https:\/\/arxiv.org\/abs\/2102.06867"},{"key":"10114_CR26","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.media.2018.12.003","volume":"52","author":"SEA Raza","year":"2019","unstructured":"Raza SEA, Cheung L, Shaban M, Graham S, Epstein D, Pelengaris S, Khan M, Rajpoot NM (2019) Micro-net: a unified model for segmentation of various objects in microscopy images. Med Image Anal 52:160\u2013173. https:\/\/doi.org\/10.1016\/j.media.2018.12.003","journal-title":"Med Image Anal"},{"key":"10114_CR27","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.media.2016.11.004","volume":"36","author":"H Chen","year":"2017","unstructured":"Chen H, Qi X, Yu L, Dou Q, Qin J, Heng P-A (2017) Dcan: deep contour-aware networks for object instance segmentation from histology images. Med Image Anal 36:135\u2013146. https:\/\/doi.org\/10.1016\/j.media.2016.11.004","journal-title":"Med Image Anal"},{"key":"10114_CR28","doi-asserted-by":"crossref","unstructured":"Zhou Y, Onder OF, Dou Q, Tsougenis E, Chen H, Heng P-A (2019) CIA-Net: Robust nuclei instance segmentation with contour-aware information aggregation. https:\/\/arxiv.org\/abs\/1903.05358","DOI":"10.1007\/978-3-030-20351-1_53"},{"issue":"19","key":"10114_CR29","doi-asserted-by":"publisher","first-page":"14403","DOI":"10.1007\/s00521-023-08394-3","volume":"35","author":"J Wang","year":"2023","unstructured":"Wang J, Qin L, Chen D, Wang J, Han B-W, Zhu Z, Qiao G (2023) An improved hover-net for nuclear segmentation and classification in histopathology images. Neural Comput Appl 35(19):14403\u201314417. https:\/\/doi.org\/10.1007\/s00521-023-08394-3","journal-title":"Neural Comput Appl"},{"key":"10114_CR30","doi-asserted-by":"publisher","unstructured":"Oda H, Roth H, Chiba K, Sokoli\u0107 J, Kitasaka T, Oda M, Hinoki A, Uchida H, Schnabel J, Mori K (2018) BESNet: boundary-enhanced segmentation of cells in histopathological images: 21st international conference, Granada, Spain, September 16\u201320, 2018. Proceedings, Part II, pp 228\u2013236. https:\/\/doi.org\/10.1007\/978-3-030-00934-2_26","DOI":"10.1007\/978-3-030-00934-2_26"},{"issue":"21","key":"10114_CR31","doi-asserted-by":"publisher","first-page":"15447","DOI":"10.1007\/s00521-023-08503-2","volume":"35","author":"SN Rashid","year":"2023","unstructured":"Rashid SN, Fraz MM (2023) Nuclei probability and centroid map network for nuclei instance segmentation in histology images. Neural Comput Appl 35(21):15447\u201360","journal-title":"Neural Comput Appl"},{"issue":"2","key":"10114_CR32","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","volume":"38","author":"P Naylor","year":"2018","unstructured":"Naylor P, La\u00e9 M, Reyal F, Walter T (2018) Segmentation of nuclei in histopathology images by deep regression of the distance map. IEEE Trans Med Imaging 38(2):448\u2013459","journal-title":"IEEE Trans Med Imaging"},{"key":"10114_CR33","doi-asserted-by":"publisher","first-page":"118945","DOI":"10.1016\/j.eswa.2022.118945","volume":"213","author":"I Ahmad","year":"2023","unstructured":"Ahmad I, Xia Y, Cui H, Islam ZU (2023) Dan-nucnet: a dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions. Expert Syst Appl 213:118945. https:\/\/doi.org\/10.1016\/j.eswa.2022.118945","journal-title":"Expert Syst Appl"},{"issue":"7","key":"10114_CR34","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TMI.2017.2677499","volume":"36","author":"N Kumar","year":"2017","unstructured":"Kumar N, Verma R, Sharma S, Bhargava S, Vahadane A, Sethi A (2017) A dataset and a technique for generalized nuclear segmentation for computational pathology. IEEE Trans Med Imaging 36(7):1550\u20131560. https:\/\/doi.org\/10.1109\/TMI.2017.2677499","journal-title":"IEEE Trans Med Imaging"},{"key":"10114_CR35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-23937-4_2","volume-title":"Digital pathology. ECDP 2019. Lecture notes in computer science","author":"J Gamper","year":"2019","unstructured":"Gamper J, Alemi\u00a0Koohbanani N, Benet K, Khuram A, Rajpoot N (2019) PanNuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification. In: Reyes-Aldasoro C, Janowczyk A, Veta M, Bankhead P, Sirinukunwattana K (eds) Digital pathology. ECDP 2019. Lecture notes in computer science, vol 11435. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-23937-4_2"},{"key":"10114_CR36","doi-asserted-by":"publisher","first-page":"104349","DOI":"10.1016\/j.compbiomed.2021.104349","volume":"132","author":"A Mahbod","year":"2021","unstructured":"Mahbod A, Schaefer G, Bancher B, L\u00f6w C, Dorffner G, Ecker R, Ellinger I (2021) Cryonuseg: a dataset for nuclei instance segmentation of cryosectioned h & e-stained histological images. Comput Biol Med 132:104349. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104349","journal-title":"Comput Biol Med"},{"key":"10114_CR37","doi-asserted-by":"publisher","unstructured":"Graham S et al (2021) Lizard: a large-scale dataset for colonic nuclear instance segmentation and classification. In: 2021 IEEE\/CVF international conference on computer vision workshops (ICCVW). Montreal, BC, Canada, pp 684\u2013693. https:\/\/doi.org\/10.1109\/ICCVW54120.2021.00082","DOI":"10.1109\/ICCVW54120.2021.00082"},{"key":"10114_CR38","doi-asserted-by":"publisher","first-page":"104199","DOI":"10.1016\/j.bspc.2022.104199","volume":"79","author":"GM Dogar","year":"2023","unstructured":"Dogar GM, Shahzad M, Fraz MM (2023) Attention augmented distance regression and classification network for nuclei instance segmentation and type classification in histology images. Biomed Signal Process Control 79:104199. https:\/\/doi.org\/10.1016\/j.bspc.2022.104199","journal-title":"Biomed Signal Process Control"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10114-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10114-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10114-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T06:12:31Z","timestamp":1726726351000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10114-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,25]]},"references-count":38,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["10114"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10114-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,7,25]]},"assertion":[{"value":"19 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study provides a summary of previously published research articles sourced from open-access platforms such as PubMed, Google Scholar, and Science Direct.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All papers included in this review article are collected from open source platforms.I confirm that all previously published research articles included in this review paper have been properly cited and are used in accordance with copyright and fair use guidelines.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The articles in this review paper are included in accordance with applicable copyright and citation guidelines.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}}]}}