Hybrid representation-enhanced sampling for Bayesian active learning in musculoskeletal segmentation of lower extremities

Loureiro A, Mills PM, Barrett RS (2013) Muscle weakness in hip osteoarthritis: a systematic review. Arthritis Care Res 65(3):340–352

Article  Google Scholar 

Uemura K, Takao M, Sakai T, Nishii T, Sugano N (2016) Volume increases of the gluteus maximus, gluteus medius, and thigh muscles after hip arthroplasty. J Arthroplast 31(4):906–912

Article  Google Scholar 

Ogawa T, Takao M, Otake Y, Yokota F, Hamada H, Sakai T, Sato Y, Sugano N (2020) Validation study of the CT-based cross-sectional evaluation of muscular atrophy and fatty degeneration around the pelvis and the femur. J Orthop Sci 25(1):139–144

Article  PubMed  Google Scholar 

Yagi M, Taniguchi M, Tateuchi H, Hirono T, Fukumoto Y, Yamagata M, Nakai R, Yamada Y, Kimura M, Ichihashi N (2022) Age-and sex-related differences of muscle cross-sectional area in iliocapsularis: a cross-sectional study. BMC Geriatr 22(1):435

Article  PubMed  PubMed Central  Google Scholar 

Sourati J, Gholipour A, Dy JG, Kurugol S, Warfield SK (2018) Active deep learning with fisher information for patch-wise semantic segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop. Springer, DLMIA, pp 83–91

Settles B, Craven M (2008) An analysis of active learning strategies for sequence labeling tasks. In: proceedings of the 2008 conference on EMNLP, pp 1070–1079

Budd S, Robinson EC, Kainz B (2021) A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med Image Anal 71:102062

Article  PubMed  Google Scholar 

Gal Y, Ghahramani Z (2016) Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning, PMLR, pp 1050–1059

Gal Y, Islam R, Ghahramani Z (2017) Deep Bayesian active learning with image data. In: International conference on machine learning. PMLR, pp 1183–1192

Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Adv Neural Inf Process Syst 30

Gaillochet M, Desrosiers C, Lombaert H (2023) Active learning for medical image segmentation with stochastic batches. Med Image Anal 102958

Yang L, Zhang Y, Chen J, Zhang S, Chen DZ (2017) Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: MICCAI. Springer, Quebec City, QC, Canada, pp 399–407

Hiasa Y, Otake Y, Takao M, Ogawa T, Sugano N, Sato Y (2019) Automated muscle segmentation from clinical CT using Bayesian u-net for personalized musculoskeletal modeling. IEEE Trans Med Imaging 39(4):1030–1040

Article  PubMed  Google Scholar 

Ozdemir F, Peng Z, Fuernstahl P, Tanner C, Goksel O (2021) Active learning for segmentation based on Bayesian sample queries. Knowl-Based Syst 214:106531

Article  Google Scholar 

Smailagic A, Costa P, Young Noh H, Walawalkar D, Khandelwal K, Galdran A, Mirshekari M, Fagert J, Xu S, Zhang P, Campilho A (2018) Medal: accurate and robust deep active learning for medical image analysis. In: ICMLA. IEEE, pp 481–488

Nath V, Yang D, Landman BA, Xu D, Roth HR (2020) Diminishing uncertainty within the training pool: active learning for medical image segmentation. IEEE Trans Med Imaging 40(10):2534–2547

Article  Google Scholar 

Li X, Xia M, Jiao J, Zhou S, Chang C, Wang Y, Guo Y (2023) HAL-IA: a hybrid active learning framework using interactive annotation for medical image segmentation. Med Image Anal 102862

Liu P, Wang L, Ranjan R, He G, Zhao L (2022) A survey on active deep learning: from model driven to data driven. ACM Comput Surv (CSUR) 54(10s):1–34

Article  Google Scholar 

Amagata D (2023) Diversity maximization in the presence of outliers. In: Proceedings of the AAAI conference on artificial intelligence, pp 12338–12345

Fukumoto Y, Taniguchi M, Hirono T, Yagi M, Yamagata M, Nakai R, Asai T, Yamada Y, Kimura M, Ichihashi N (2022) Influence of ultrasound focus depth on the association between echo intensity and intramuscular adipose tissue. Muscle Nerve 66(5):568–575

Rosset A, Spadola L, Ratib O (2004) OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging 17:205–216

Article  PubMed  PubMed Central  Google Scholar 

Kikinis R, Pieper SD, Vosburgh KG (2013) 3d slicer: a platform for subject-specific image analysis, visualization, and clinical support. In: Intraoperative imaging and image-guided therapy. Springer, pp 277–289

Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980–2988

Chen JA, Niu W, Ren B, Wang Y, Shen X (2023) Survey: exploiting data redundancy for optimization of deep learning. ACM Comput Surv 55(10):1–38

Article  Google Scholar 

Yuan J, Hou X, Xiao Y, Cao D, Guan W, Nie L (2019) Multi-criteria active deep learning for image classification. Knowl-Based Syst 172:86–94

Article  Google Scholar 

Nath V, Yang D, Roth HR, Xu D (2022) Warm start active learning with proxy labels and selection via semi-supervised fine-tuning. In: MICCAI. Springer, Singapore, pp 297–308

留言 (0)

沒有登入
gif