{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T07:26:33Z","timestamp":1777879593714,"version":"3.51.4"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110425","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:55:51Z","timestamp":1776977751000},"page":"110425","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Walking the energy lines: Physics-inspired fuzzy self-ensembling for medical image classification"],"prefix":"10.1016","volume":"121","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9709-5912","authenticated-orcid":false,"given":"Saurabh","family":"Sharma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9339-9872","authenticated-orcid":false,"given":"Atul","family":"Kumar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5994-9024","authenticated-orcid":false,"given":"Joydeep","family":"Chandra","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110425_b1","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","first-page":"163","article-title":"Categorical relation-preserving contrastive knowledge distillation for medical image classification","author":"Xing","year":"2021"},{"issue":"11","key":"10.1016\/j.bspc.2026.110425_b2","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1109\/TMI.2020.2995518","article-title":"Semi-supervised medical image classification with relation-driven self-ensembling model","volume":"39","author":"Liu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110425_b3","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","first-page":"133","article-title":"Confidence matters: Enhancing medical image classification through uncertainty-driven contrastive self-distillation","author":"Sharma","year":"2024"},{"key":"10.1016\/j.bspc.2026.110425_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112503","article-title":"StAlK: Structural alignment based self knowledge distillation for medical image classification","volume":"304","author":"Sharma","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.bspc.2026.110425_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2025.103134","article-title":"Healing with hierarchy: Hierarchical attention empowered graph neural networks for predictive analysis in medical data","volume":"165","author":"Gupta","year":"2025","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bspc.2026.110425_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102693","article-title":"SSD-KD: A self-supervised diverse knowledge distillation method for lightweight skin lesion classification using dermoscopic images","volume":"84","author":"Wang","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.bspc.2026.110425_b7","series-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","author":"Tarvainen","year":"2017"},{"key":"10.1016\/j.bspc.2026.110425_b8","series-title":"The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023","article-title":"Exploring the role of mean teachers in Self-supervised masked auto-encoders","author":"Lee","year":"2023"},{"key":"10.1016\/j.bspc.2026.110425_b9","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"12","article-title":"Skin lesion classification in dermoscopy images using synergic deep learning","author":"Zhang","year":"2018"},{"key":"10.1016\/j.bspc.2026.110425_b10","series-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"key":"10.1016\/j.bspc.2026.110425_b11","series-title":"European Conference on Computer Vision","first-page":"588","article-title":"Knowledge distillation meets self-supervision","author":"Xu","year":"2020"},{"key":"10.1016\/j.bspc.2026.110425_b12","doi-asserted-by":"crossref","first-page":"62830","DOI":"10.1109\/ACCESS.2020.2983774","article-title":"Sequence-Dropout block for reducing overfitting problem in image classification","volume":"8","author":"Qian","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.bspc.2026.110425_b13","article-title":"Analyzing overfitting under class imbalance in neural networks for image segmentation","author":"Li","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.110425_b14","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.bspc.2026.110425_b15","series-title":"Rethinking the inception architecture for computer vision","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.bspc.2026.110425_b16","unstructured":"Rafael M\u00fcller, Simon Kornblith, Geoffrey E. Hinton, When does label smoothing help?, in: Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d\u2019Alch\u00e9-Buc, Emily B. Fox, Roman Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 4696\u20134705."},{"key":"10.1016\/j.bspc.2026.110425_b17","article-title":"A physics-informed knowledge distillation model with spatial\u2013temporal attention for energy consumption pre-assessment in sustainable additive manufacturing","volume":"60","author":"Wang","year":"2023","journal-title":"Sustain. Energy Technol. Assess."},{"key":"10.1016\/j.bspc.2026.110425_b18","series-title":"Proceedings of the 32nd ACM International Conference on Multimedia","first-page":"3431","article-title":"Reversing structural pattern learning with biologically inspired knowledge distillation for spiking neural networks","author":"Xu","year":"2024"},{"key":"10.1016\/j.bspc.2026.110425_b19","series-title":"APTOS 2019 blindness detection","author":"Karthik","year":"2019"},{"issue":"1","key":"10.1016\/j.bspc.2026.110425_b20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.161","article-title":"The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions","volume":"5","author":"Tschandl","year":"2018","journal-title":"Sci. Data"},{"key":"10.1016\/j.bspc.2026.110425_b21","series-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","first-page":"168","article-title":"Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)","author":"Codella","year":"2018"},{"key":"10.1016\/j.bspc.2026.110425_b22","series-title":"KVASIR: A multi-class image dataset for computer aided gastrointestinal disease detection","author":"Pogorelov","year":"2017"},{"key":"10.1016\/j.bspc.2026.110425_b23","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1016\/j.patcog.2016.05.032","article-title":"Exploring illumination robust descriptors for human epithelial type 2 cell classification","volume":"60","author":"Qi","year":"2016","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.bspc.2026.110425_b24","series-title":"2023 Third International Conference on Secure Cyber Computing and Communication","first-page":"149","article-title":"Diabetic retinopathy severity classification based on attention mechanism","author":"Jha","year":"2023"},{"issue":"9","key":"10.1016\/j.bspc.2026.110425_b25","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/TMI.2019.2893944","article-title":"Attention residual learning for skin lesion classification","volume":"38","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110425_b26","series-title":"International Conference on Information Processing in Medical Imaging","first-page":"793","article-title":"Melanoma recognition via visual attention","author":"Yan","year":"2019"},{"key":"10.1016\/j.bspc.2026.110425_b27","series-title":"Annual Conference on Medical Image Understanding and Analysis","first-page":"3","article-title":"Exploring the correlation between deep learned and clinical features in melanoma detection","author":"Chowdhury","year":"2021"},{"key":"10.1016\/j.bspc.2026.110425_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108549","article-title":"A novel multi-task learning network for skin lesion classification based on multi-modal clues and label-level fusion","volume":"175","author":"Lin","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110425_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111624","article-title":"Explainable deep inherent learning for multi-classes skin lesion classification","volume":"159","author":"Hosny","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.bspc.2026.110425_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2026.109553","article-title":"LCTKAN: Lightweight convolution Transformer\u2013KAN for medical image classification","volume":"116","author":"Liu","year":"2026","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110425_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.109420","article-title":"SACE-Net: Scale-adaptive and context-enriched network for medical image classification","volume":"115","author":"Tu","year":"2026","journal-title":"Biomed. Signal Process. Control."},{"key":"10.1016\/j.bspc.2026.110425_b32","article-title":"PromptMed: Prompt-driven semi-supervised medical image classification with class-balanced consistency and contrastive learning","author":"Wang","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110425_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2026.109538","article-title":"DB-HDFFN: Dual branch hierarchical dynamic feature fusion network for medical image classification","volume":"117","author":"Wen","year":"2026","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.bspc.2026.110425_b34","series-title":"2025 IEEE Biomedical Circuits and Systems Conference (BioCAS)","first-page":"224","article-title":"Medmambalite: Hardware-aware mamba for medical image classification","author":"Aalishah","year":"2025"},{"issue":"6","key":"10.1016\/j.bspc.2026.110425_b35","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: A survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.bspc.2026.110425_b36","series-title":"FitNets: Hints for thin deep nets","author":"Romero","year":"2014"},{"key":"10.1016\/j.bspc.2026.110425_b37","series-title":"Relational knowledge distillation","author":"Park","year":"2019"},{"key":"10.1016\/j.bspc.2026.110425_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119060","article-title":"Distilling and transferring knowledge via cGAN-generated samples for image classification and regression","volume":"213","author":"Ding","year":"2023","journal-title":"Expert Syst. Appl."},{"issue":"23","key":"10.1016\/j.bspc.2026.110425_b39","doi-asserted-by":"crossref","first-page":"28520","DOI":"10.1007\/s10489-023-05036-y","article-title":"SCL-IKD: intermediate knowledge distillation via supervised contrastive representation learning","volume":"53","author":"Sharma","year":"2023","journal-title":"Appl. Intell."},{"key":"10.1016\/j.bspc.2026.110425_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2021.102176","article-title":"Classification of diabetic retinopathy using unlabeled data and knowledge distillation","volume":"121","author":"Abbasi","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bspc.2026.110425_b41","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.108671","article-title":"Temperature-driven robust disease detection in brain and gastrointestinal disorders via context-aware adaptive knowledge distillation","volume":"112","author":"Khan","year":"2026","journal-title":"Biomed. Signal Process. Control."},{"issue":"4","key":"10.1016\/j.bspc.2026.110425_b42","doi-asserted-by":"crossref","DOI":"10.1145\/3746229","article-title":"Activation map-based knowledge distillation for real-time cervical OCT image classification","volume":"24","author":"Wang","year":"2025","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"10.1016\/j.bspc.2026.110425_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.127145","article-title":"Multiple teachers are beneficial: A lightweight and noise-resistant student model for point-of-care imaging classification","volume":"275","author":"Song","year":"2025","journal-title":"Expert Syst. Appl."},{"issue":"15","key":"10.1016\/j.bspc.2026.110425_b44","doi-asserted-by":"crossref","DOI":"10.3390\/electronics14153115","article-title":"Ensemble-based knowledge distillation for identification of childhood pneumonia","volume":"14","author":"Vrban\u010di\u010d","year":"2025","journal-title":"Electronics"},{"key":"10.1016\/j.bspc.2026.110425_b45","series-title":"2024 25th International Arab Conference on Information Technology","first-page":"1","article-title":"Leveraging knowledge distillation in vision transformers for binary classification of kidney tumors from CT radiography images","author":"Abimouloud","year":"2024"},{"key":"10.1016\/j.bspc.2026.110425_b46","series-title":"2025 25th International Conference on Digital Signal Processing","first-page":"1","article-title":"Enhancing few-shot medical image classification with supervised patch-token knowledge distillation","author":"Karampinis","year":"2025"},{"issue":"7","key":"10.1016\/j.bspc.2026.110425_b47","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics15070929","article-title":"A distillation approach to transformer-based medical image classification with limited data","volume":"15","author":"Sevinc","year":"2025","journal-title":"Diagnostics"},{"issue":"5","key":"10.1016\/j.bspc.2026.110425_b48","doi-asserted-by":"crossref","first-page":"3455","DOI":"10.1109\/TETCI.2025.3526259","article-title":"Uncertainty driven adaptive self-knowledge distillation for medical image segmentation","volume":"9","author":"Guo","year":"2025","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"10.1016\/j.bspc.2026.110425_b49","series-title":"International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.bspc.2026.110425_b50","unstructured":"Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick, Momentum contrast for unsupervised visual representation learning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 9729\u20139738."},{"key":"10.1016\/j.bspc.2026.110425_b51","series-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","author":"Zagoruyko","year":"2016"},{"key":"10.1016\/j.bspc.2026.110425_b52","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3733","article-title":"Unsupervised feature learning via non-parametric instance discrimination","author":"Wu","year":"2018"},{"key":"10.1016\/j.bspc.2026.110425_b53","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1214\/aoms\/1177703732","article-title":"Robust estimation of a location parameter","volume":"35","author":"Huber","year":"1964","journal-title":"Ann. Math. Stat."},{"key":"10.1016\/j.bspc.2026.110425_b54","series-title":"Advances in Neural Information Processing Systems 32","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.bspc.2026.110425_b55","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.bspc.2026.110425_b56","doi-asserted-by":"crossref","unstructured":"Gao Huang, Zhuang Liu, Laurens Van Der Maaten, Kilian Q Weinberger, Densely connected convolutional networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.bspc.2026.110425_b57","series-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"12","key":"10.1016\/j.bspc.2026.110425_b58","doi-asserted-by":"crossref","first-page":"3820","DOI":"10.1109\/TMI.2021.3098703","article-title":"Efficient medical image segmentation based on knowledge distillation","volume":"40","author":"Qin","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110425_b59","series-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.bspc.2026.110425_b60","series-title":"MobileNetV2: Inverted residuals and linear bottlenecks","author":"Sandler","year":"2018"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009791?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009791?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T23:48:12Z","timestamp":1777592892000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":60,"alternative-id":["S1746809426009791"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110425","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Walking the energy lines: Physics-inspired fuzzy self-ensembling for medical image classification","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110425","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110425"}}