{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T05:10:21Z","timestamp":1748668221794,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031781971"},{"type":"electronic","value":"9783031781988"}],"license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78198-8_19","type":"book-chapter","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T09:03:48Z","timestamp":1733216628000},"page":"283-300","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Medical Image Analysis with MA-DTNet: A Dual Task Network Guided by Morphological Attention"],"prefix":"10.1007","author":[{"given":"Susmita","family":"Ghosh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Swagatam","family":"Das","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"19_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 28, 104863 (2020)","journal-title":"Data Brief"},{"key":"19_CR2","volume-title":"AAU-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images","author":"G Chen","year":"2022","unstructured":"Chen, G., Li, L., Dai, Y., Zhang, J., Yap, M.H.: AAU-net: an adaptive attention u-net for breast lesions segmentation in ultrasound images. IEEE Trans. Med, Imaging (2022)"},{"key":"19_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109728","volume":"142","author":"G Chen","year":"2023","unstructured":"Chen, G., Li, L., Zhang, J., Dai, Y.: Rethinking the unpretentious u-net for medical ultrasound image segmentation. Pattern Recogn. 142, 109728 (2023)","journal-title":"Pattern Recogn."},{"key":"19_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123265","volume":"246","author":"G Chen","year":"2024","unstructured":"Chen, G., Zhou, L., Zhang, J., Yin, X., Cui, L., Dai, Y.: Esknet: An enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation. Expert Syst. Appl. 246, 123265 (2024)","journal-title":"Expert Syst. Appl."},{"key":"19_CR5","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the ECCV. pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"6","key":"19_CR6","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1109\/TMI.2022.3142321","volume":"41","author":"J Cheng","year":"2022","unstructured":"Cheng, J., Liu, J., Kuang, H., Wang, J.: A fully automated multimodal MRI-based multi-task learning for glioma segmentation and IDH genotyping. IEEE Trans. Med. Imaging 41(6), 1520\u20131532 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"19_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/01617346221075769","volume":"44","author":"J Chowdary","year":"2022","unstructured":"Chowdary, J., Yogarajah, P., Chaurasia, P., Guruviah, V.: A multi-task learning framework for automated segmentation and classification of breast tumors from ultrasound images. Ultrason. Imaging 44(1), 3\u201312 (2022)","journal-title":"Ultrason. Imaging"},{"issue":"1","key":"19_CR8","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1002\/mp.15341","volume":"49","author":"W Cui","year":"2022","unstructured":"Cui, W., Peng, Y., Yuan, G., et al.: FMRNet: A fused network of multiple tumoral regions for breast tumor classification with ultrasound images. Med. Phys. 49(1), 144\u2013157 (2022)","journal-title":"Med. Phys."},{"key":"19_CR9","doi-asserted-by":"crossref","unstructured":"Dabass, M., Vashisth, ., Vig, R.: MTU: A multi-tasking u-net with hybrid convolutional learning and attention modules for cancer classification and gland segmentation in colon histopathological images. Comput. Biol. Med. 150, 106095 (2022)","DOI":"10.1016\/j.compbiomed.2022.106095"},{"issue":"03","key":"19_CR10","doi-asserted-by":"publisher","first-page":"242","DOI":"10.4103\/0971-3026.54878","volume":"19","author":"S Gokhale","year":"2009","unstructured":"Gokhale, S.: Ultrasound characterization of breast masses. Indian Journal of Radiology and Imaging 19(03), 242\u2013247 (2009)","journal-title":"Indian Journal of Radiology and Imaging"},{"key":"19_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"19_CR12","doi-asserted-by":"crossref","unstructured":"Kim, S., Purdie, T.G., McIntosh, C.: Cross-task attention network: Improving multi-task learning for medical imaging applications. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 119\u2013128. Springer (2023)","DOI":"10.1007\/978-3-031-47401-9_12"},{"key":"19_CR13","unstructured":"Luo, H., Changdong, Y., Selvan, R.: Hybrid ladder transformers with efficient parallel-cross attention for medical image segmentation. In: International Conference on Medical Imaging with Deep Learning. pp. 808\u2013819. PMLR (2022)"},{"key":"19_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104425","volume":"81","author":"Y Lyu","year":"2023","unstructured":"Lyu, Y., Xu, Y., Jiang, X., Liu, J., Zhao, X., Zhu, X.: Ams-pan: Breast ultrasound image segmentation model combining attention mechanism and multi-scale features. Biomed. Signal Process. Control 81, 104425 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"19_CR15","doi-asserted-by":"crossref","unstructured":"Mo, Y., Han, C., Liu, Y., Liu, M., Shi, Z., Lin, J., Zhao, B., Huang, C., Qiu, B., Cui, Y., et\u00a0al.: Hover-trans: Anatomy-aware hover-transformer for roi-free breast cancer diagnosis in ultrasound images. IEEE Transactions on Medical Imaging (2023)","DOI":"10.1109\/TMI.2023.3236011"},{"key":"19_CR16","doi-asserted-by":"crossref","unstructured":"Mondal, R., Purkait, P., Santra, S., Chanda, B.: Morphological networks for image de-raining. In: International Conference on Discrete Geometry for Computer Imagery. pp. 262\u2013275. Springer (2019)","DOI":"10.1007\/978-3-030-14085-4_21"},{"issue":"2","key":"19_CR17","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/TMI.2021.3116087","volume":"41","author":"Z Ning","year":"2021","unstructured":"Ning, Z., Zhong, S., Feng, Q., Chen, W., Zhang, Y.: Smu-net: Saliency-guided morphology-aware u-net for breast lesion segmentation in ultrasound image. IEEE Trans. Med. Imaging 41(2), 476\u2013490 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR18","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"19_CR19","doi-asserted-by":"crossref","unstructured":"Roy, S.K., Mondal, R., Paoletti, M.E., Haut, J.M., Plaza, A.: Morphological convolutional neural networks for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 14, 8689\u20138702 (2021)","DOI":"10.1109\/JSTARS.2021.3088228"},{"key":"19_CR20","unstructured":"Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)"},{"key":"19_CR21","doi-asserted-by":"crossref","unstructured":"Singh, V.K., Mohamed, E.M., Abdel-Nasser, M.: Aggregating efficient transformer and cnn networks using learnable fuzzy measure for breast tumor malignancy prediction in ultrasound images. Neural Computing and Applications pp. 1\u201317 (2024)","DOI":"10.1007\/s00521-023-09363-6"},{"key":"19_CR22","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.media.2016.08.008","volume":"35","author":"K Sirinukunwattana","year":"2017","unstructured":"Sirinukunwattana, K., Pluim, J.P., Chen, H., et al.: Gland segmentation in colon histology images: The glas challenge contest. Med. Image Anal. 35, 489\u2013502 (2017)","journal-title":"Med. Image Anal."},{"issue":"1","key":"19_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Scientific data 5(1), 1\u20139 (2018)","journal-title":"Scientific data"},{"key":"19_CR24","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"key":"19_CR25","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: Convolutional block attention module. In: Proceedings of the ECCV. pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Wu, H., Huang, X., Guo, X., Wen, Z., Qin, J.: Cross-image dependency modelling for breast ultrasound segmentation. IEEE Trans. Med, Imaging (2023)","DOI":"10.1109\/TMI.2022.3233648"},{"issue":"7","key":"19_CR27","doi-asserted-by":"publisher","first-page":"2482","DOI":"10.1109\/TMI.2020.2972964","volume":"39","author":"Y Xie","year":"2020","unstructured":"Xie, Y., Zhang, J., Xia, Y., Shen, C.: A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans. Med. Imaging 39(7), 2482\u20132493 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Xu, M., Huang, K., Qi, X.: Multi-task learning with context-oriented self-attention for breast ultrasound image classification and segmentation. In: 2022 IEEE 19th ISBI. pp.\u00a01\u20135. IEEE (2022)","DOI":"10.1109\/ISBI52829.2022.9761685"},{"key":"19_CR29","doi-asserted-by":"publisher","first-page":"5377","DOI":"10.1109\/ACCESS.2023.3236693","volume":"11","author":"M Xu","year":"2023","unstructured":"Xu, M., Huang, K., Qi, X.: A regional-attentive multi-task learning framework for breast ultrasound image segmentation and classification. IEEE Access 11, 5377\u20135392 (2023)","journal-title":"IEEE Access"},{"issue":"4","key":"19_CR30","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","volume":"22","author":"MH Yap","year":"2017","unstructured":"Yap, M.H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., Davison, A.K., Marti, R.: Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J. Biomed. Health Inform. 22(4), 1218\u20131226 (2017)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"19_CR31","doi-asserted-by":"crossref","unstructured":"Zeng, W., Fan, W., Chen, R., et\u00a0al.: Accurate 3d kidney segmentation using unsupervised domain translation and adversarial networks. In: 2021 IEEE 18th ISBI. pp. 598\u2013602. IEEE (2021)","DOI":"10.1109\/ISBI48211.2021.9434099"},{"issue":"10","key":"19_CR32","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1007\/s11548-021-02445-7","volume":"16","author":"G Zhang","year":"2021","unstructured":"Zhang, G., Zhao, K., Hong, Y., Qiu, X., Zhang, K., Wei, B.: SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification. Int. J. Comput. Assist. Radiol. Surg. 16(10), 1719\u20131725 (2021). https:\/\/doi.org\/10.1007\/s11548-021-02445-7","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"issue":"12","key":"19_CR33","doi-asserted-by":"publisher","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Yang, Q.: A survey on multi-task learning. IEEE Trans. Knowl. Data Eng. 34(12), 5586\u20135609 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"19_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107346","volume":"230","author":"S Zhong","year":"2023","unstructured":"Zhong, S., Tu, C., Dong, X., Feng, Q., Chen, W., Zhang, Y.: MsGoF: breast lesion classification on ultrasound images by multi-scale gradational-order fusion framework. Comput. Methods Programs Biomed. 230, 107346 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"key":"19_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0S., M.M., Tajbakhsh, N., et\u00a0al.: Unet++: A nested u-net architecture for medical image segmentation. In: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. pp. 3\u201311. Springer (2018)","DOI":"10.1007\/978-3-030-00889-5_1"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78198-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T10:20:11Z","timestamp":1733221211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78198-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"ISBN":["9783031781971","9783031781988"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78198-8_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"4 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}