{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T21:00:12Z","timestamp":1776891612028,"version":"3.51.2"},"reference-count":84,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T00:00:00Z","timestamp":1666915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology of Taiwan","award":["MOST109-2221-E-011-018-MY3"],"award-info":[{"award-number":["MOST109-2221-E-011-018-MY3"]}]},{"name":"Ministry of Science and Technology of Taiwan","award":["MOST 111-2320-B-016-009"],"award-info":[{"award-number":["MOST 111-2320-B-016-009"]}]},{"name":"Ministry of Science and Technology of Taiwan","award":["NTUST-TSGH-111-05"],"award-info":[{"award-number":["NTUST-TSGH-111-05"]}]},{"name":"Ministry of Science and Technology of Taiwan","award":["TSGH-D-109094"],"award-info":[{"award-number":["TSGH-D-109094"]}]},{"name":"Ministry of Science and Technology of Taiwan","award":["TSGH-D-110036"],"award-info":[{"award-number":["TSGH-D-110036"]}]},{"name":"Ministry of Science and Technology of Taiwan","award":["TSGH-A-111010"],"award-info":[{"award-number":["TSGH-A-111010"]}]},{"name":"Ministry of Science and Technology of Taiwan","award":["TSGH-A-112008"],"award-info":[{"award-number":["TSGH-A-112008"]}]},{"name":"National Taiwan University of Science and Technology-Tri-Service General Hospital","award":["MOST109-2221-E-011-018-MY3"],"award-info":[{"award-number":["MOST109-2221-E-011-018-MY3"]}]},{"name":"National Taiwan University of Science and Technology-Tri-Service General Hospital","award":["MOST 111-2320-B-016-009"],"award-info":[{"award-number":["MOST 111-2320-B-016-009"]}]},{"name":"National Taiwan University of Science and Technology-Tri-Service General Hospital","award":["NTUST-TSGH-111-05"],"award-info":[{"award-number":["NTUST-TSGH-111-05"]}]},{"name":"National Taiwan University of Science and Technology-Tri-Service General Hospital","award":["TSGH-D-109094"],"award-info":[{"award-number":["TSGH-D-109094"]}]},{"name":"National Taiwan University of Science and Technology-Tri-Service General Hospital","award":["TSGH-D-110036"],"award-info":[{"award-number":["TSGH-D-110036"]}]},{"name":"National Taiwan University of Science and Technology-Tri-Service General Hospital","award":["TSGH-A-111010"],"award-info":[{"award-number":["TSGH-A-111010"]}]},{"name":"National Taiwan University of Science and Technology-Tri-Service General Hospital","award":["TSGH-A-112008"],"award-info":[{"award-number":["TSGH-A-112008"]}]},{"name":"Tri-Service General Hospital, Taipei, Taiwan","award":["MOST109-2221-E-011-018-MY3"],"award-info":[{"award-number":["MOST109-2221-E-011-018-MY3"]}]},{"name":"Tri-Service General Hospital, Taipei, Taiwan","award":["MOST 111-2320-B-016-009"],"award-info":[{"award-number":["MOST 111-2320-B-016-009"]}]},{"name":"Tri-Service General Hospital, Taipei, Taiwan","award":["NTUST-TSGH-111-05"],"award-info":[{"award-number":["NTUST-TSGH-111-05"]}]},{"name":"Tri-Service General Hospital, Taipei, Taiwan","award":["TSGH-D-109094"],"award-info":[{"award-number":["TSGH-D-109094"]}]},{"name":"Tri-Service General Hospital, Taipei, Taiwan","award":["TSGH-D-110036"],"award-info":[{"award-number":["TSGH-D-110036"]}]},{"name":"Tri-Service General Hospital, Taipei, Taiwan","award":["TSGH-A-111010"],"award-info":[{"award-number":["TSGH-A-111010"]}]},{"name":"Tri-Service General Hospital, Taipei, Taiwan","award":["TSGH-A-112008"],"award-info":[{"award-number":["TSGH-A-112008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>According to the World Health Organization Report 2022, cancer is the most common cause of death contributing to nearly one out of six deaths worldwide. Early cancer diagnosis and prognosis have become essential in reducing the mortality rate. On the other hand, cancer detection is a challenging task in cancer pathology. Trained pathologists can detect cancer, but their decisions are subjective to high intra- and inter-observer variability, which can lead to poor patient care owing to false-positive and false-negative results. In this study, we present a soft label fully convolutional network (SL-FCN) to assist in breast cancer target therapy and thyroid cancer diagnosis, using four datasets. To aid in breast cancer target therapy, the proposed method automatically segments human epidermal growth factor receptor 2 (HER2) amplification in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images. To help in thyroid cancer diagnosis, the proposed method automatically segments papillary thyroid carcinoma (PTC) on Papanicolaou-stained fine needle aspiration and thin prep whole slide images (WSIs). In the evaluation of segmentation of HER2 amplification in FISH and DISH images, we compare the proposed method with thirteen deep learning approaches, including U-Net, U-Net with InceptionV5, Ensemble of U-Net with Inception-v4, Inception-Resnet-v2 encoder, and ResNet-34 encoder, SegNet, FCN, modified FCN, YOLOv5, CPN, SOLOv2, BCNet, and DeepLabv3+ with three different backbones, including MobileNet, ResNet, and Xception, on three clinical datasets, including two DISH datasets on two different magnification levels and a FISH dataset. The result on DISH breast dataset 1 shows that the proposed method achieves high accuracy of 87.77 \u00b1 14.97%, recall of 91.20 \u00b1 7.72%, and F1-score of 81.67 \u00b1 17.76%, while, on DISH breast dataset 2, the proposed method achieves high accuracy of 94.64 \u00b1 2.23%, recall of 83.78 \u00b1 6.42%, and F1-score of 85.14 \u00b1 6.61% and, on the FISH breast dataset, the proposed method achieves high accuracy of 93.54 \u00b1 5.24%, recall of 83.52 \u00b1 13.15%, and F1-score of 86.98 \u00b1 9.85%, respectively. Furthermore, the proposed method outperforms most of the benchmark approaches by a significant margin (p\u00a0&lt;0.001). In evaluation of segmentation of PTC on Papanicolaou-stained WSIs, the proposed method is compared with three deep learning methods, including Modified FCN, U-Net, and SegNet. The experimental result demonstrates that the proposed method achieves high accuracy of 99.99 \u00b1 0.01%, precision of 92.02 \u00b1 16.6%, recall of 90.90 \u00b1 14.25%, and F1-score of 89.82 \u00b1 14.92% and significantly outperforms the baseline methods, including U-Net and FCN (p\u00a0&lt;0.001). With the high degree of accuracy, precision, and recall, the results show that the proposed method could be used in assisting breast cancer target therapy and thyroid cancer diagnosis with faster evaluation and minimizing human judgment errors.<\/jats:p>","DOI":"10.3390\/cancers14215312","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T23:45:00Z","timestamp":1667087100000},"page":"5312","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9992-6863","authenticated-orcid":false,"given":"Ching-Wei","family":"Wang","sequence":"first","affiliation":[{"name":"Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"},{"name":"Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuan-Yu","family":"Lin","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8073-8659","authenticated-orcid":false,"given":"Yi-Jia","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Pathology, Tri-Service General Hospital, Taipei 11490, Taiwan"},{"name":"Institute of Pathology and Parasitology, National Defense Medical Center, Taipei 11490, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad-Adil","family":"Khalil","sequence":"additional","affiliation":[{"name":"Graduate Institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai-Lin","family":"Chu","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei 106335, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8219-6382","authenticated-orcid":false,"given":"Tai-Kuang","family":"Chao","sequence":"additional","affiliation":[{"name":"Department of Pathology, Tri-Service General Hospital, Taipei 11490, Taiwan"},{"name":"Institute of Pathology and Parasitology, National Defense Medical Center, Taipei 11490, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","first-page":"84","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","article-title":"U-Net: Deep learning for cell counting, detection, and morphometry","volume":"16","author":"Falk","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102270","DOI":"10.1016\/j.media.2021.102270","article-title":"Deep learning for bone marrow cell detection and classification on whole-slide images","volume":"75","author":"Wang","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lin, Y.J., Chao, T.K., Khalil, M.A., Lee, Y.C., Hong, D.Z., Wu, J.J., and Wang, C.W. (2021). Deep Learning Fast Screening Approach on Cytological Whole Slides for Thyroid Cancer Diagnosis. Cancers, 13.","DOI":"10.3390\/cancers13153891"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"16244","DOI":"10.1038\/s41598-021-95545-y","article-title":"Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning","volume":"11","author":"Wang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"990","DOI":"10.3390\/diagnostics12040990","article-title":"Fast Segmentation of Metastatic Foci in H&E Whole-Slide Images for Breast Cancer Diagnosis","volume":"12","author":"Khalil","year":"2022","journal-title":"Diagnostics"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, C.W., Khalil, M.A., Lin, Y.J., Lee, Y.C., Huang, T.W., and Chao, T.K. (2022). Deep Learning Using Endobronchial-Ultrasound-Guided Transbronchial Needle Aspiration Image to Improve the Overall Diagnostic Yield of Sampling Mediastinal Lymphadenopathy. Diagnostics, 12.","DOI":"10.3390\/diagnostics12092234"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, C.W., Lee, Y.C., Chang, C.C., Lin, Y.J., Liou, Y.A., Hsu, P.C., Chang, C.C., Sai, A.K.O., Wang, C.H., and Chao, T.K. (2022). A Weakly Supervised Deep Learning Method for Guiding Ovarian Cancer Treatment and Identifying an Effective Biomarker. Cancers, 14.","DOI":"10.3390\/cancers14071651"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"102093","DOI":"10.1016\/j.compmedimag.2022.102093","article-title":"Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images","volume":"99","author":"Wang","year":"2022","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Pol\u00f3nia, A., and Campilho, A. (2017). Classification of breast cancer histology images using convolutional neural networks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177544"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1038\/modpathol.2008.83","article-title":"High concordance between immunohistochemistry and fluorescence in situ hybridization testing for HER2 status in breast cancer requires a normalized IHC scoring system","volume":"21","author":"Gown","year":"2008","journal-title":"Mod. Pathol."},{"key":"ref_15","first-page":"166","article-title":"HER2-directed therapy for metastatic breast cancer","volume":"27","author":"Jelovac","year":"2013","journal-title":"Oncol. Williston Park"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1200\/JCO.2002.20.3.719","article-title":"Efficacy and safety of trastuzumab as a single agent in first-line treatment of HER2-overexpressing metastatic breast cancer","volume":"20","author":"Vogel","year":"2002","journal-title":"J. Clin. Oncol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1056\/NEJMoa052306","article-title":"Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer","volume":"353","author":"Procter","year":"2005","journal-title":"N. Engl. J. Med."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/S1470-2045(09)70087-7","article-title":"Lapatinib monotherapy in patients with HER2-overexpressing relapsed or refractory inflammatory breast cancer: Final results and survival of the expanded HER2+ cohort in EGF103009, a phase II study","volume":"10","author":"Kaufman","year":"2009","journal-title":"Lancet Oncol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e49","DOI":"10.1016\/j.critrevonc.2010.09.002","article-title":"Therapeutic strategies and mechanisms of tumorigenesis of HER2-overexpressing breast cancer","volume":"84","author":"Emde","year":"2012","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1001\/jama.2015.17463","article-title":"ERBB2 (HER2) testing in breast cancer","volume":"315","author":"Hilal","year":"2016","journal-title":"JAMA"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4278","DOI":"10.1002\/cncr.33102","article-title":"Novel HER2\u2013targeted therapies for HER2\u2013positive metastatic breast cancer","volume":"126","author":"Kunte","year":"2020","journal-title":"Cancer"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1001\/jamaoncol.2018.6012","article-title":"Assessment of ERBB2\/HER2 status in HER2-equivocal breast cancers by FISH and 2013\/2014 ASCO-CAP guidelines","volume":"5","author":"Press","year":"2019","journal-title":"JAMA Oncol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s10549-018-4755-5","article-title":"Immunohistochemistry and alternative FISH testing in breast cancer with HER2 equivocal amplification","volume":"170","author":"Agersborg","year":"2018","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1002\/cncy.22184","article-title":"HER2 assessment by bright-field dual in situ hybridization in cell blocks of recurrent and metastatic breast carcinoma","volume":"127","author":"Edelweiss","year":"2019","journal-title":"Cancer Cytopathol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.5858\/arpa.2019-0510-OA","article-title":"HER2 dual in situ hybridization: Correlations and cautions","volume":"144","author":"Troxell","year":"2020","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10549-019-05148-5","article-title":"Impact of the updated 2018 ASCO\/CAP guidelines on HER2 FISH testing in invasive breast cancer: A retrospective study of HER2 fish results of 2233 cases","volume":"175","author":"Liu","year":"2019","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1056\/NEJM200103153441101","article-title":"Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2","volume":"344","author":"Slamon","year":"2001","journal-title":"N. Engl. J. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.1200\/JCO.2003.02.018","article-title":"Trastuzumab and vinorelbine as first-line therapy for HER2-overexpressing metastatic breast cancer: Multicenter phase II trial with clinical outcomes, analysis of serum tumor markers as predictive factors, and cardiac surveillance algorithm","volume":"21","author":"Burstein","year":"2003","journal-title":"J. Clin. Oncol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-018-0305-z","article-title":"Artificial intelligence in healthcare","volume":"2","author":"Yu","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-44643-z","article-title":"Automated detection of the HER2 gene amplification status in Fluorescence in situ hybridization images for the diagnostics of cancer tissues","volume":"9","author":"Zakrzewski","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1038\/s41571-019-0252-y","article-title":"Artificial intelligence in digital pathology\u2014New tools for diagnosis and precision oncology","volume":"16","author":"Bera","year":"2019","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_33","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.crfs.2021.10.003","article-title":"Detection of mold on the food surface using YOLOv5","volume":"4","author":"Jubayer","year":"2021","journal-title":"Curr. Res. Food Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"102371","DOI":"10.1016\/j.media.2022.102371","article-title":"Contour Proposal Networks for Biomedical Instance Segmentation","volume":"77","author":"Upschulte","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_38","first-page":"17721","article-title":"Solov2: Dynamic and fast instance segmentation","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ke, L., Tai, Y.W., and Tang, C.K. (2021, January 20\u201325). Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00401"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_41","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101665","DOI":"10.1016\/j.media.2020.101665","article-title":"Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks","volume":"61","author":"Wang","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"101814","DOI":"10.1016\/j.media.2020.101814","article-title":"Weakly supervised instance learning for thyroid malignancy prediction from whole slide cytopathology images","volume":"67","author":"Dov","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"102038","DOI":"10.1016\/j.media.2021.102038","article-title":"SoftSeg: Advantages of soft versus binary training for image segmentation","volume":"71","author":"Gros","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_46","unstructured":"M\u00fcller, R., Kornblith, S., and Hinton, G. (2019, January 8\u201314). When Does Label Smoothing Help?. Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Kats, E., Goldberger, J., and Greenspan, H. (2019, January 13\u201317). A soft STAPLE algorithm combined with anatomical knowledge. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China.","DOI":"10.1007\/978-3-030-32248-9_57"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24571-3"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5984","DOI":"10.1109\/TIP.2021.3089942","article-title":"Delving deep into label smoothing","volume":"30","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Van Engelen, A., Niessen, W., Klein, S., Verhagen, H., Groen, H., Wentzel, J., Lugt, A., and de Bruijne, M. (2012, January 2\u20135). Supervised in-vivo plaque characterization incorporating class label uncertainty. Proceedings of the 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), Barcelona, Spain.","DOI":"10.1109\/ISBI.2012.6235530"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2366","DOI":"10.1161\/01.ATV.0000240518.90124.57","article-title":"In vivo characterization and quantification of atherosclerotic carotid plaque components with multidetector computed tomography and histopathological correlation","volume":"26","author":"Ouhlous","year":"2006","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.1109\/TCSVT.2020.2983600","article-title":"Progressive Cross-Camera Soft-Label Learning for Semi-Supervised Person Re-Identification","volume":"30","author":"Qi","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/TMI.2004.828354","article-title":"Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation","volume":"23","author":"Warfield","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, H., Wei, D., Cao, S., Ma, K., Wang, L., and Zheng, Y. (2020). Superpixel-Guided Label Softening for Medical Image Segmentation, Springer.","DOI":"10.1007\/978-3-030-59719-1_23"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.neucom.2020.03.127","article-title":"Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels","volume":"437","author":"Pham","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1007\/s00371-019-01726-2","article-title":"Automatic semantic style transfer using deep convolutional neural networks and soft masks","volume":"36","author":"Zhao","year":"2020","journal-title":"Vis. Comput."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chorowski, J., and Jaitly, N. (2016). Towards better decoding and language model integration in sequence to sequence models. arXiv.","DOI":"10.21437\/Interspeech.2017-343"},{"key":"ref_58","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_59","unstructured":"Kuijf, H.J., and Bennink, E. (2022, August 01). Grand Challenge on MR Brain Segmentation at MICCAI 2018. Available online: https:\/\/mrbrains18.isi.uu.nl."},{"key":"ref_60","unstructured":"Krizhevsky, A., Nair, V., and Hinton, G. (2022, August 01). CIFAR-100 (Canadian Institute for Advanced Research). Available online: https:\/\/cs.toronto.edu\/~kriz\/cifar.html."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Shen, J., Li, T., Hu, C., He, H., Jiang, D., and Liu, J. (2019, January 23\u201327). An Augmented Cell Segmentation in Fluorescent in Situ Hybridization Images. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856923"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Shen, J., Li, T., Hu, C., He, H., and Liu, J. (2019, January 13). Automatic cell segmentation using mini-u-net on fluorescence in situ hybridization images. Proceedings of the Medical Imaging 2019: Computer-Aided Diagnosis, SPIE, San Diego, CA, USA.","DOI":"10.1117\/12.2513394"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nmeth.2083","article-title":"Annotated high-throughput microscopy image sets for validation","volume":"9","author":"Ljosa","year":"2012","journal-title":"Nat. Methods"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"18","DOI":"10.5858\/2007-131-18-ASOCCO","article-title":"American Society of Clinical Oncology\/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer","volume":"131","author":"Wolff","year":"2007","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1126\/science.3798106","article-title":"Human breast cancer: Correlation of relapse and survival with amplification of the HER-2\/neu oncogene","volume":"235","author":"Slamon","year":"1987","journal-title":"Science"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1158\/1078-0432.CCR-18-2275","article-title":"Advances in HER2-targeted therapy: Novel agents and opportunities beyond breast and gastric cancer","volume":"25","author":"Johnson","year":"2019","journal-title":"Clin. Cancer Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1023\/A:1024579206250","article-title":"HER-2\/neu assessment in primary chemotherapy treated breast carcinoma: No evidence of gene profile changing","volume":"80","author":"Dagrada","year":"2003","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.5858\/2003-127-1451-NSIBCM","article-title":"Her-2\/neu status in breast cancer metastases to the central nervous system","volume":"127","author":"Yoon","year":"2003","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1023\/A:1021874224490","article-title":"Comparison of topoisomerase-IIalpha gene status between primary breast cancer and corresponding distant metastatic sites","volume":"77","author":"Durbecq","year":"2003","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1093\/jnci\/djs317","article-title":"Risk of heart failure in breast cancer patients after anthracycline and trastuzumab treatment: A retrospective cohort study","volume":"104","author":"Bowles","year":"2012","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_73","first-page":"13","article-title":"Trastuzumab-mediated cardiotoxicity: Current understanding, challenges, and frontiers","volume":"1","author":"Mohan","year":"2018","journal-title":"Antib. Ther."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3747\/co.22.2363","article-title":"Targeted therapy in her2-positive metastatic breast cancer: A review of the literature","volume":"22","author":"Zhu","year":"2015","journal-title":"Curr. Oncol."},{"key":"ref_75","first-page":"418","article-title":"Correlation between immunohistochemistry (HercepTest) and fluorescence in situ hybridization (FISH) for HER-2 in 426 breast carcinomas from 37 centres","volume":"199","author":"Dowsett","year":"2003","journal-title":"J. Pathol. J. Pathol. Soc. Great Br. Irel."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1038\/bjc.2014.147","article-title":"Impact of HER2 copy number in IHC2+\/FISH-amplified breast cancer on outcome of adjuvant trastuzumab treatment in a large UK cancer network","volume":"110","author":"Borley","year":"2014","journal-title":"Br. J. Cancer"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s12282-015-0664-1","article-title":"Bright-field HER2 dual in situ hybridization (DISH) assay on breast cancer cell blocks: A comparative study with histological sections","volume":"23","author":"Nishimura","year":"2016","journal-title":"Breast Cancer"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"553","DOI":"10.5858\/arpa.2013-0314-SA","article-title":"Determination of HER2\/neu status: A pilot study comparing HER2\/neu dual in situ hybridization DNA probe cocktail assay performed on cell blocks to immunohistochemisty and fluorescence in situ hybridization performed on histologic specimens","volume":"138","author":"Hartman","year":"2014","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","volume":"318","author":"Bejnordi","year":"2017","journal-title":"JAMA"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"33985","DOI":"10.1038\/srep33985","article-title":"Multi-Pass Adaptive Voting for nuclei detection in histopathological images","volume":"6","author":"Lu","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"5","DOI":"10.4103\/jpi.jpi_74_17","article-title":"Deep learning nuclei detection in digitized histology images by superpixels","volume":"9","author":"Sornapudi","year":"2018","journal-title":"J. Pathol. Inform."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"034003","DOI":"10.1117\/1.JMI.1.3.034003","article-title":"Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features","volume":"1","author":"Wang","year":"2014","journal-title":"J. Med. Imaging Bellingham"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1002\/cyto.a.23375","article-title":"Semantic segmentation of mFISH images using convolutional networks","volume":"93","author":"Pardo","year":"2018","journal-title":"Cytom. Part A"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.cmpb.2019.03.006","article-title":"Automated density-based counting of FISH amplification signals for HER2 status assessment","volume":"173","author":"Homeyer","year":"2019","journal-title":"Comput. Methods Programs Biomed."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/21\/5312\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:05:20Z","timestamp":1760144720000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/14\/21\/5312"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,28]]},"references-count":84,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["cancers14215312"],"URL":"https:\/\/doi.org\/10.3390\/cancers14215312","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,28]]}}}