{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T04:46:02Z","timestamp":1785818762044,"version":"3.56.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s11517-025-03301-5","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T10:23:54Z","timestamp":1737627834000},"page":"1697-1713","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Adaptive ensemble loss and multi-scale attention in breast ultrasound segmentation with UMA-Net"],"prefix":"10.1007","volume":"63","author":[{"given":"Mohsin Furkh","family":"Dar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Avatharam","family":"Ganivada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"3301_CR1","doi-asserted-by":"publisher","first-page":"104863","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"Walid Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani Walid, Gomaa Mohammed, Khaled Hussien, Fahmy Aly (2020) Dataset of breast ultrasound images. Data in Brief 28:104863","journal-title":"Data in Brief"},{"key":"3301_CR2","doi-asserted-by":"crossref","unstructured":"Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK (2018) Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. arXiv:1802.06955","DOI":"10.1109\/NAECON.2018.8556686"},{"issue":"3","key":"3301_CR3","doi-asserted-by":"publisher","first-page":"2126","DOI":"10.1109\/TETCI.2024.3377676","volume":"8","author":"MY Ansari","year":"2024","unstructured":"Ansari MY, Mangalote IA, Meher PK, Aboumarzouk O, Al-Ansari A, Halabi O, Dakua SP (2024) Advancements in deep learning for B-mode ultrasound segmentation: a comprehensive review. IEEE Trans Emerging Top Comput Intell 8(3):2126\u20132149","journal-title":"IEEE Trans Emerging Top Comput Intell"},{"key":"3301_CR4","doi-asserted-by":"crossref","unstructured":"Avola D, Cinque L, Fagioli A, Foresti G, Mecca A (2021) Ultrasound medical imaging techniques: a survey. ACM Comput Surv 54(3)","DOI":"10.1145\/3447243"},{"key":"3301_CR5","first-page":"1","volume":"PP","author":"R Azad","year":"2024","unstructured":"Azad R, Aghdam EK, Rauland A, Jia Y, Avval AH, Bozorgpour A, Karimijafarbigloo S, Cohen JP, Adeli E, Merhof D (2024) Medical image segmentation review: the success of U-Net. IEEE Trans Pattern Anal Mach Intell PP:1\u201320","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3301_CR6","doi-asserted-by":"publisher","first-page":"106531","DOI":"10.1016\/j.cmpb.2021.106531","volume":"213","author":"X Bian","year":"2022","unstructured":"Bian X, Luo X, Wang C, Liu W, Lin X (2022) DDA-Net: unsupervised cross-modality medical image segmentation via dual domain adaptation. Comput Methods Progr Biomed 213:106531","journal-title":"Comput Methods Progr Biomed"},{"issue":"9","key":"3301_CR7","doi-asserted-by":"publisher","first-page":"15999","DOI":"10.3934\/mbe.2023713","volume":"20","author":"B Cai","year":"2023","unstructured":"Cai B, Qing X, Yang C, Yi L, Ge C, Wang Z, Liu K, Qiu X, Chang S (2023) Spine MRI image segmentation method based on ASPP and U-Net network. Math Biosci Eng 20(9):15999\u201316014","journal-title":"Math Biosci Eng"},{"key":"3301_CR8","unstructured":"Chen L-C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv:1706.05587"},{"issue":"11","key":"3301_CR9","doi-asserted-by":"publisher","first-page":"13408","DOI":"10.1109\/TPAMI.2023.3289667","volume":"45","author":"Y Chen","year":"2023","unstructured":"Chen Y, Yu L, Wang JY, Panjwani N, Obeid JP, Liu W, Liu L, Kovalchuk N, Gensheimer MF, Vitzthum LK (2023) Beadle BM adaptive region-specific loss for improved medical image segmentation. IEEE Trans Pattern Anal Mach Intell 45(11):13408\u201313421","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"3301_CR10","doi-asserted-by":"publisher","first-page":"13408","DOI":"10.1109\/TPAMI.2023.3289667","volume":"45","author":"Y Chen","year":"2023","unstructured":"Chen Y, Yu L, Wang JY, Panjwani N, Obeid JP, Liu W, Liu L, Kovalchuk N, Gensheimer MF, Vitzthum LK, Beadle BM, Chang DT, Le Q-T, Han B, Xing L (2023) Adaptive region-specific loss for improved medical image segmentation. IEEE Trans Pattern Anal Mach Intell 45(11):13408\u201313421","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3301_CR11","doi-asserted-by":"publisher","first-page":"10439","DOI":"10.1007\/s11063-023-11333-x","volume":"55","author":"MF Dar","year":"2023","unstructured":"Dar MF, Ganivada A (2023) EfficientU-Net: a novel deep learning method for breast tumor segmentation and classification in ultrasound images. Neural Process Lett 55:10439\u201310462","journal-title":"Neural Process Lett"},{"key":"3301_CR12","doi-asserted-by":"publisher","first-page":"105018","DOI":"10.1016\/j.imavis.2024.105018","volume":"146","author":"MF Dar","year":"2024","unstructured":"Dar MF, Ganivada A (2024) Deep learning and genetic algorithm-based ensemble model for feature selection and classification of breast ultrasound images. Image Vis Comput 146:105018","journal-title":"Image Vis Comput"},{"key":"3301_CR13","doi-asserted-by":"crossref","unstructured":"Guo P, Su X, Zhang H, Wang M, Bao F (2020) A multi-scaled receptive field learning approach for medical image segmentation, ICASSP 2020 - 2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1414\u20131418","DOI":"10.1109\/ICASSP40776.2020.9054030"},{"key":"3301_CR14","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications, arXiv:1704.04861"},{"key":"3301_CR15","doi-asserted-by":"crossref","unstructured":"Kamran SA, Hossain KF, Tavakkoli A, Zuckerbrod SL, Sanders KM, Baker SA (2021) RV-GAN: segmenting retinal vascular structure in fundus photographs using a novel multi-scale generative adversarial network. In: de\u00a0Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, Essert C (eds) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021 (Cham), Springer International Publishing, pp 34\u201344","DOI":"10.1007\/978-3-030-87237-3_4"},{"issue":"2","key":"3301_CR16","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1109\/TMI.2019.2930068","volume":"39","author":"D Karimi","year":"2020","unstructured":"Karimi D, Salcudean SE (2020) Reducing the Hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans Med Imaging 39(2):499\u2013513","journal-title":"IEEE Trans Med Imaging"},{"key":"3301_CR17","doi-asserted-by":"publisher","first-page":"120183","DOI":"10.1016\/j.ins.2024.120183","volume":"661","author":"Q Lin","year":"2024","unstructured":"Lin Q, Chen X, Chen C, Garibaldi JM (2024) Boundary-wise loss for medical image segmentation based on fuzzy rough sets. Inf Sci 661:120183","journal-title":"Inf Sci"},{"key":"3301_CR18","doi-asserted-by":"publisher","first-page":"106822","DOI":"10.1016\/j.bspc.2024.106822","volume":"99","author":"J Liu","year":"2025","unstructured":"Liu J, Shao J, Sen X, Tang Z, Liu W, Li Z, Wang T, Bian X (2025) ASYM-UNet: an asymmetric u-shape network for breast lesions ultrasound images segmentation. Biomed Signal Process Control 99:106822","journal-title":"Biomed Signal Process Control"},{"key":"3301_CR19","doi-asserted-by":"publisher","first-page":"103077","DOI":"10.1016\/j.bspc.2021.103077","volume":"71","author":"D Maji","year":"2022","unstructured":"Maji D, Sigedar P, Singh M (2022) Attention Res-UNet with guided decoder for semantic segmentation of brain tumors. Biomed Signal Process Control 71:103077","journal-title":"Biomed Signal Process Control"},{"key":"3301_CR20","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Fourth international conference on 3D vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"3301_CR21","doi-asserted-by":"crossref","unstructured":"Nam J-H, Syazwany NS, Kim SJ, Lee S-C (2024) Modality-agnostic domain generalizable medical image segmentation by multi-frequency in multi-scale attention, In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition pp 11480\u201311491","DOI":"10.1109\/CVPR52733.2024.01091"},{"issue":"2","key":"3301_CR22","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/TMI.2021.3116087","volume":"41","author":"Z Ning","year":"2022","unstructured":"Ning Z, Zhong S, Feng Q, Chen W, Zhang Y (2022) SMU-Net: saliency-guided morphology-aware U-Net for breast lesion segmentation in ultrasound image. IEEE Trans Med Imaging 41(2):476\u2013490","journal-title":"IEEE Trans Med Imaging"},{"key":"3301_CR23","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention U-Net: learning where to look for the pancreas. Medical Imaging with Deep Learning"},{"issue":"18","key":"3301_CR24","doi-asserted-by":"publisher","first-page":"12023","DOI":"10.1007\/s00521-021-05908-9","volume":"33","author":"D Peng","year":"2021","unstructured":"Peng D, Xi Y, Peng W, Jianping L (2021) DGFAU-Net: global feature attention upsampling network for medical image segmentation. Neural Comput Appl 33(18):12023\u201312037","journal-title":"Neural Comput Appl"},{"key":"3301_CR25","unstructured":"Rodrigues PS (2017) Breast ultrasound image, Mendeley Data, V1"},{"key":"3301_CR26","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.patcog.2018.01.032","volume":"79","author":"A Rodtook","year":"2018","unstructured":"Rodtook A, Kirimasthong K, Lohitvisate W, Makhanov SS (2018) Automatic initialization of active contours and level set method in ultrasound images of breast abnormalities. Pattern Recognit 79:172\u2013182","journal-title":"Pattern Recognit"},{"key":"3301_CR27","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF (eds) Medical image computing and computer-assisted intervention \u2013 MICCAI 2015 (Cham). Springer International Publishing, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3301_CR28","doi-asserted-by":"publisher","first-page":"106884","DOI":"10.1016\/j.compbiomed.2023.106884","volume":"159","author":"R Jintao","year":"2023","unstructured":"Jintao R, Beichen L, Chen B, Shi J, Chen G, Wang M, Pan Z, Lin Y, Gao Z, Zhou J, Liu X, Zhang C (2023) Attention guided neural ode network for breast tumor segmentation in medical images. Comput Biol Med 159:106884","journal-title":"Comput Biol Med"},{"key":"3301_CR29","doi-asserted-by":"crossref","unstructured":"Salehi SSM, Erdogmus D, Gholipour A (2017) Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang Q, Shi Y, Suk H-I, Suzuki K, (eds) Machine learning in medical imaging (Cham). Springer International Publishing, pp.\u00a0379\u2013387","DOI":"10.1007\/978-3-319-67389-9_44"},{"key":"3301_CR30","doi-asserted-by":"publisher","first-page":"82031","DOI":"10.1109\/ACCESS.2021.3086020","volume":"9","author":"N Siddique","year":"2021","unstructured":"Siddique N, Paheding S, Elkin CP, Devabhaktuni V (2021) U-Net and its variants for medical image segmentation: a review of theory and applications. IEEE Access 9:82031\u201382057","journal-title":"IEEE Access"},{"issue":"1","key":"3301_CR31","first-page":"17","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel RL, Miller KD, Wagle NS, Jemal A (2023) Cancer statistics 2023. CA: Cancer J Clin 73(1):17\u201348","journal-title":"CA: Cancer J Clin"},{"key":"3301_CR32","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.compmedimag.2019.04.005","volume":"75","author":"SA Taghanaki","year":"2019","unstructured":"Taghanaki SA, Zheng Y, Zhou SK, Georgescu B, Sharma P, Xu D, Comaniciu D, Hamarneh G (2019) Combo loss: handling input and output imbalance in multi-organ segmentation. Comput Med Imaging Graph 75:24\u201333","journal-title":"Comput Med Imaging Graph"},{"issue":"1","key":"3301_CR33","doi-asserted-by":"publisher","first-page":"015018","DOI":"10.1088\/1361-6560\/ad1319","volume":"69","author":"J Tasnim","year":"2023","unstructured":"Tasnim J, Hasan MK (2023) CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images. Phys Med Biol 69(1):015018","journal-title":"Phys Med Biol"},{"key":"3301_CR34","doi-asserted-by":"publisher","first-page":"106073","DOI":"10.1016\/j.compbiomed.2022.106073","volume":"149","author":"RA Dar","year":"2022","unstructured":"Dar RA, Rasool M, Assad A (2022) Breast cancer detection using deep learning: datasets, methods, and challenges ahead. Comput Biol Med 149:106073","journal-title":"Comput Biol Med"},{"key":"3301_CR35","doi-asserted-by":"publisher","first-page":"108006","DOI":"10.1016\/j.patcog.2021.108006","volume":"118","author":"W Jipeng","year":"2021","unstructured":"Jipeng W, Haibo X, Zhang S, Li X, Chen J, Zheng J, Gao Y, Tian Y, Liang Y, Ji R (2021) Joint segmentation and detection of COVID-19 via a sequential region generation network. Pattern Recognit 118:108006","journal-title":"Pattern Recognit"},{"issue":"1","key":"3301_CR36","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TMI.2022.3192303","volume":"43","author":"Q Xie","year":"2024","unstructured":"Xie Q, Li Y, He N, Ning M, Ma K, Wang G, Lian Y, Zheng Y (2024) Unsupervised domain adaptation for medical image segmentation by disentanglement learning and self-training. IEEE Trans Med Imaging 43(1):4\u201314","journal-title":"IEEE Trans Med Imaging"},{"key":"3301_CR37","unstructured":"Xie Y, Yang B, Guan Q, Zhang J, Wu Q, Xia Y (2023) Attention mechanisms in medical image segmentation: a survey. ArXiv:2305.17937"},{"key":"3301_CR38","doi-asserted-by":"crossref","unstructured":"Xu C, Qi Y, Wang Y, Lou M, Pi J, Ma Y (2022) ARF-Net: an adaptive receptive field network for breast mass segmentation in whole mammograms and ultrasound images. Biomed Signal Process Control 71:103178. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1746809421007758","DOI":"10.1016\/j.bspc.2021.103178"},{"key":"3301_CR39","doi-asserted-by":"publisher","first-page":"106740","DOI":"10.1016\/j.bspc.2024.106740","volume":"98","author":"X Yang","year":"2024","unstructured":"Yang X, Li X, Qin Y, Wang H, Zhao C, Yin Y (2024) Interlayer information fusion-based and dual-attention improved U-Net for ABVS image sequence intelligent tumor segmentation. Biomed Signal Process Control 98:106740","journal-title":"Biomed Signal Process Control"},{"key":"3301_CR40","doi-asserted-by":"crossref","unstructured":"Yap MH, Pons G, Marti J, Ganau S, Sentis M, Zwiggelaar R, Davison AK, Marti R (2018) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Inform 22:1218\u20131226","DOI":"10.1109\/JBHI.2017.2731873"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03301-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03301-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03301-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T05:34:56Z","timestamp":1748237696000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03301-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,23]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["3301"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03301-5","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,23]]},"assertion":[{"value":"4 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No humans\/animals were involved in the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}