Adaptive Dual-Axis Style-Based Recalibration Network With Class-Wise Statistics Loss for Imbalanced Medical Image Classification Xiaoqing Zhang, Zunjie Xiao, Jingzhe Ma, Xiao Wu, Jilu Zhao, et al. IEEE Transactions on Image Processing, 2025 Salient and small lesions (e.g., microaneurysms on fundus) both play significant roles in real-world disease diagnosis under medical image examinations. Although deep neural networks (DNNs) have achieved promising medical image classification performance, they often have limitations in capturing both salient and small lesion information, restricting performance improvement in imbalanced medical image classification. Recently, with the advent of DNN-based style transfer in medical image generation, the roles of clinical styles have attracted great interest, as they are crucial indicators of lesions. Motivated by this observation, we propose a novel Adaptive Dual-Axis Style-based Recalibration (ADSR) module, leveraging the potential of clinical styles to guide DNNs in effectively learning salient and small lesion information from a dual-axis perspective. ADSR first emphasizes salient lesion information via global style-based adaptation, then captures small lesion information with pixel-wise style-based fusion. We construct an ADSR-Net for imbalanced medical image classification by stacking multiple ADSR modules. Additionally, DNNs typically adopt cross-entropy loss for parameter optimization, which ignores the impacts of class-wise predicted probability distributions. To address this, we introduce a new Class-wise Statistics Loss (CWS) combined with CE to further boost imbalanced medical image classification results. Extensive experiments on five imbalanced medical image datasets demonstrate not only the superiority of ADSR-Net and CWS over state-of-the-art (SOTA) methods but also their improved confidence calibration results. For example, ADSR-Net with the proposed loss significantly outperforms CABNet50 by 21.39% and 27.82% in F1 and B-ACC while reducing 3.31% and 4.57% in ECE and BS on ISIC2018.
Is it feasible to develop a supervised learning algorithm incorporating spinopelvic mobility to predict impingement in patients undergoing total hip arthroplasty? A proof-of-concept study Andreas Fontalis, Baixiang Zhao, Pierre Putzeys, Fabio Mancino, Shuai Zhang, et al. Bone and Joint Open, 2024 AimsPrecise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement.MethodsThis international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy.ResultsWe identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%).ConclusionThis study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential.Cite this article: Bone Jt Open 2024;5(8):671–680.
3D Reconstruction of Tibia and Fibula using One General Model and Two X-ray Images Kai Pan, Shuai Zhang, Liang Zhao, Shoudong Huang, Yanhao Zhang, et al. Proceedings IEEE International Conference on Robotics and Automation, 2023 The 3D reconstruction of patient specific bone models plays a crucial role in orthopaedic surgery for clinical evaluation, surgical planning and precise implant design or selection. This paper considers the problem of reconstructing a patient-specific 3D tibia and fibula model from only two 2D X-ray images and one 3D general model segmented from the lower leg CT scans of one randomly selected patient. Currently, the bone 3D reconstruction mainly relies on computed tomography (CT) and magnetic resonance imaging (MRI) scanning-based mode segmentation which result in high radiation exposure or expensive costs. While, the proposed algorithm can accurately and efficiently deform a 3D general model to achieve a patient-specific 3D model that matches the patient's tibia and fibula projections in two 2D X-rays. The algorithm undergoes a preliminary deformation, 2D contour registration, and opti-misation based on the deformation graph that represents the shape deformation of models. Evaluations using simulations, cadaver and in-vivo experiments demonstrate that the proposed algorithm can effectively reconstruct the patient's 3D tibia and fibula surface model with high accuracy.
A Template-Based 3D Reconstruction of Colon Structures and Textures from Stereo Colonoscopic Images Shuai Zhang, Liang Zhao, Shoudong Huang, Menglong Ye, Qi Hao IEEE Transactions on Medical Robotics and Bionics, 2021 This article presents a framework for 3D reconstruction of colonic surface using stereo colonoscopic images. Due to the limited overlaps between consecutive frames and the nonexistence of large loop closures during a normal screening colonoscopy, the state-of-art simultaneous localization and mapping (SLAM) algorithms cannot be directly applied to this scenario, thus a colon model segmented from CT scans is used together with the colonosocopic images to achieve the colon 3D reconstruction with high accuracy. The proposed framework includes 3D scan (point cloud with RGB information) reconstruction from stereo images, a visual odometry (VO) based camera pose initialization module, a 3D registration scheme for matching texture scans to the segmented colon model, and a barycentric-based texture rendering module for mapping textures from colonoscopic images to the reconstructed colonic surface. A realistic simulator is developed using Unity to simulate the procedures of colonoscopy and used to provide experimental datasets in different scenarios. Experimental results demonstrate the good performance of the proposed 3D colonic surface reconstruction method in terms of accuracy and robustness. Currently, the framework requires a pre-operative colon model as the template for colon reconstruction and can reconstruct 3D colon maps when the colon has no large deformation and the colon structure is clearly visible. The datasets used in this article and the developed simulator are made publicly available for other researchers to use (https://github.com/zsustc/colon_reconstruction_dataset).
3D Acetabular Surface Reconstruction from 2D Pre-operative X-Ray Images Using SRVF Elastic Registration and Deformation Graph (MICCAI, Oral) S Zhang, J Wang, X Wang, S Konan, D Stoyanov, EB Mazomenos International Conference on Medical Image Computing and Computer-Assisted … , 2025 2025
Adjunct tools for colonoscopy enhancement: a comprehensive review NN Dei, EB Mazomenos, S Zhang, S Bano, JMM Montiel, D Stoyanov, ... IEEE Transactions on Medical Robotics and Bionics , 2025 2025 Citations: 2
Endolrmgs: Complete endoscopic scene reconstruction combining large reconstruction modelling and gaussian splatting X Wang, S Zhang, B Huang, D Stoyanov, EB Mazomenos arXiv preprint arXiv:2503.22437 , 2025 2025 Citations: 3
Adaptive dual-axis style-based recalibration network with class-wise statistics loss for imbalanced medical image classification X Zhang, Z Xiao, J Ma, X Wu, J Zhao, S Zhang, R Li, Y Pan, J Liu IEEE Transactions on Image Processing , 2025 2025 Citations: 30
StereoMamba: Real-Time and Robust Intraoperative Stereo Disparity Estimation via Long-Range Spatial Dependencies. X Wang, J Xu, S Zhang, B Huang, D Stoyanov, EB Mazomenos IEEE Robotics and Automation Letters, 10682-10689 , 2025 2025
Direct Camera-Only Bundle Adjustment for 3-D Textured Colon Surface Reconstruction Based on Pre-Operative Model S Zhang, L Zhao, S Huang, EB Mazomenos, D Stoyanov IEEE Transactions on Medical Robotics and Bionics 7 (1), 242-253 , 2024 2024 Citations: 1
Gaussian pancakes: geometrically-regularized 3d gaussian splatting for realistic endoscopic reconstruction S Bonilla, S Zhang, D Psychogyios, D Stoyanov, F Vasconcelos, S Bano International Conference on Medical Image Computing and Computer-Assisted … , 2024 2024 Citations: 38
SLAM-TKA: Simultaneously Localizing X-Ray Device and Mapping Pins in Conventional Total Knee Arthroplasty S Zhang, L Zhao, S Huang, H Wang, Q Luo, Q Hao, D Stoyanov IEEE Transactions on Medical Robotics and Bionics 6 (4), 1526-1541 , 2024 2024 Citations: 2
Is it feasible to develop a supervised learning algorithm incorporating spinopelvic mobility to predict impingement in patients undergoing total hip arthroplasty?: a proof-of … A Fontalis, B Zhao, P Putzeys, F Mancino, S Zhang, T Vanspauwen, ... Bone & Joint Open 5 (8), 671-680 , 2024 2024 Citations: 8
3d reconstruction of tibia and fibula using one general model and two x-ray images K Pan, S Zhang, L Zhao, S Huang, Y Zhang, H Wang, Q Luo 2023 IEEE International Conference on Robotics and Automation (ICRA), 4732-4738 , 2023 2023 Citations: 5
3D Reconstruction of Colon Structures and Textures from Colonoscopic Videos S Zhang PQDT-Global , 2023 2023
SLAM-TKA: Real-time intra-operative measurement of tibial resection plane in conventional total knee arthroplasty (MICCAI Oral & Travel award) S Zhang, L Zhao, S Huang, H Wang, Q Luo, Q Hao International Conference on Medical Image Computing and Computer-Assisted … , 2022 2022 Citations: 6
3D reconstruction of deformable colon structures based on preoperative model and deep neural network S Zhang, L Zhao, S Huang, R Ma, B Hu, Q Hao 2021 IEEE International Conference on Robotics and Automation (ICRA), 1875-1881 , 2021 2021 Citations: 12
A template-based 3D reconstruction of colon structures and textures from stereo colonoscopic images S Zhang, L Zhao, S Huang, M Ye, Q Hao IEEE Transactions on Medical Robotics and Bionics 3 (1), 85-95 , 2020 2020 Citations: 53
Linear Bayesian filter based low-cost UWB systems for indoor mobile robot localization S Zhang, R Han, W Huang, S Wang, Q Hao 2018 IEEE SENSORS, 1-4 , 2018 2018 Citations: 12
An integrated uav navigation system based on geo-registered 3d point cloud S Zhang, S Wang, C Li, G Liu, Q Hao 2017 IEEE International Conference on Multisensor Fusion and Integration for … , 2017 2017 Citations: 3
A camera-based real-time polarization sensor and its application to mobile robot navigation S Zhang, H Liang, H Zhu, D Wang, B Yu 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014 … , 2014 2014 Citations: 13
A bionic camera-based polarization navigation sensor D Wang, H Liang, H Zhu, S Zhang Sensors 14 (7), 13006-13023 , 2014 2014 Citations: 90
MOST CITED SCHOLAR PUBLICATIONS
A bionic camera-based polarization navigation sensor D Wang, H Liang, H Zhu, S Zhang Sensors 14 (7), 13006-13023 , 2014 2014 Citations: 90
A template-based 3D reconstruction of colon structures and textures from stereo colonoscopic images S Zhang, L Zhao, S Huang, M Ye, Q Hao IEEE Transactions on Medical Robotics and Bionics 3 (1), 85-95 , 2020 2020 Citations: 53
Gaussian pancakes: geometrically-regularized 3d gaussian splatting for realistic endoscopic reconstruction S Bonilla, S Zhang, D Psychogyios, D Stoyanov, F Vasconcelos, S Bano International Conference on Medical Image Computing and Computer-Assisted … , 2024 2024 Citations: 38
Adaptive dual-axis style-based recalibration network with class-wise statistics loss for imbalanced medical image classification X Zhang, Z Xiao, J Ma, X Wu, J Zhao, S Zhang, R Li, Y Pan, J Liu IEEE Transactions on Image Processing , 2025 2025 Citations: 30
A camera-based real-time polarization sensor and its application to mobile robot navigation S Zhang, H Liang, H Zhu, D Wang, B Yu 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014 … , 2014 2014 Citations: 13
3D reconstruction of deformable colon structures based on preoperative model and deep neural network S Zhang, L Zhao, S Huang, R Ma, B Hu, Q Hao 2021 IEEE International Conference on Robotics and Automation (ICRA), 1875-1881 , 2021 2021 Citations: 12
Linear Bayesian filter based low-cost UWB systems for indoor mobile robot localization S Zhang, R Han, W Huang, S Wang, Q Hao 2018 IEEE SENSORS, 1-4 , 2018 2018 Citations: 12
Is it feasible to develop a supervised learning algorithm incorporating spinopelvic mobility to predict impingement in patients undergoing total hip arthroplasty?: a proof-of … A Fontalis, B Zhao, P Putzeys, F Mancino, S Zhang, T Vanspauwen, ... Bone & Joint Open 5 (8), 671-680 , 2024 2024 Citations: 8
SLAM-TKA: Real-time intra-operative measurement of tibial resection plane in conventional total knee arthroplasty (MICCAI Oral & Travel award) S Zhang, L Zhao, S Huang, H Wang, Q Luo, Q Hao International Conference on Medical Image Computing and Computer-Assisted … , 2022 2022 Citations: 6
3d reconstruction of tibia and fibula using one general model and two x-ray images K Pan, S Zhang, L Zhao, S Huang, Y Zhang, H Wang, Q Luo 2023 IEEE International Conference on Robotics and Automation (ICRA), 4732-4738 , 2023 2023 Citations: 5
Endolrmgs: Complete endoscopic scene reconstruction combining large reconstruction modelling and gaussian splatting X Wang, S Zhang, B Huang, D Stoyanov, EB Mazomenos arXiv preprint arXiv:2503.22437 , 2025 2025 Citations: 3
An integrated uav navigation system based on geo-registered 3d point cloud S Zhang, S Wang, C Li, G Liu, Q Hao 2017 IEEE International Conference on Multisensor Fusion and Integration for … , 2017 2017 Citations: 3
Adjunct tools for colonoscopy enhancement: a comprehensive review NN Dei, EB Mazomenos, S Zhang, S Bano, JMM Montiel, D Stoyanov, ... IEEE Transactions on Medical Robotics and Bionics , 2025 2025 Citations: 2
SLAM-TKA: Simultaneously Localizing X-Ray Device and Mapping Pins in Conventional Total Knee Arthroplasty S Zhang, L Zhao, S Huang, H Wang, Q Luo, Q Hao, D Stoyanov IEEE Transactions on Medical Robotics and Bionics 6 (4), 1526-1541 , 2024 2024 Citations: 2
Direct Camera-Only Bundle Adjustment for 3-D Textured Colon Surface Reconstruction Based on Pre-Operative Model S Zhang, L Zhao, S Huang, EB Mazomenos, D Stoyanov IEEE Transactions on Medical Robotics and Bionics 7 (1), 242-253 , 2024 2024 Citations: 1
3D Acetabular Surface Reconstruction from 2D Pre-operative X-Ray Images Using SRVF Elastic Registration and Deformation Graph (MICCAI, Oral) S Zhang, J Wang, X Wang, S Konan, D Stoyanov, EB Mazomenos International Conference on Medical Image Computing and Computer-Assisted … , 2025 2025
StereoMamba: Real-Time and Robust Intraoperative Stereo Disparity Estimation via Long-Range Spatial Dependencies. X Wang, J Xu, S Zhang, B Huang, D Stoyanov, EB Mazomenos IEEE Robotics and Automation Letters, 10682-10689 , 2025 2025
3D Reconstruction of Colon Structures and Textures from Colonoscopic Videos S Zhang PQDT-Global , 2023 2023
