Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Untitled work

(2014) · Cited by 33,419

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation

Milletari, F., Navab, N., & Ahmadi, S.-A. (2016) · 2016 Fourth International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2016.79 · Cited by 11,931

Fully convolutional networks for semantic segmentation

Shelhamer, E., Long, J., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2572683 · Cited by 11,898

Ensemble methods in machine learning

Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408

3D Microscopic Images Segmenter Modeling by Applying Two‐Stage Optimization to an Ensemble of Segmentation Methods Using a Genetic Algorithm

Kafadar, M., Avdagić, Z., Bešić, I., & Omanović, S. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70058 · Cited by 1

The Worrisome Impact of an Inter-rater Bias on Neural Network Training

Shwartzman, O., Gazit, H., Ben-Aryeh, G., Shalef, I., & Raviv, T. R. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-3863-5_42 · Cited by 2

Relation U-Net

He, S., Bao, R., Grant, P. E., & Ou, Y. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980668 · Cited by 1

Adaptive-AutoMO: A domain adaptive automated multiobjective neural network for reliable lesion malignancy prediction via digital breast tomosynthesis

Chen, X., Lv, J., Wang, Z., Qin, G., & Zhou, Z. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104869 · Cited by 1

Is this good enough? On expert perception of brain tumor segmentation quality

Hoebel, K., Bridge, C. P., Ahmed, S., Akintola, O. O., Chung, C. D., Huang, R. Y., Johnson, J. M., Kim, A. E., Ly, K. I., Chang, K., et al. (2022) · SPIE Medical Imaging: Image Perception, Observer Performance, and Technology Assessment · doi:10.1117/12.2611810 · Cited by 2

What Do Untargeted Adversarial Examples Reveal in Medical Image Segmentation?

Park, G., Hong, C., Kim, B., & Kim, W. H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16749-2_5 · Cited by 1

Dose Guidance for Radiotherapy-Oriented Deep Learning Segmentation

Rüfenacht, E., Poel, R., Kamath, A. J., Ermiş, E., Scheib, S. G., Fix, M. K., & Reyes, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43996-4_50 · Cited by 1

Probabilistic Spatial Analysis in Quantitative Microscopy with Uncertainty-Aware Cell Detection using Deep Bayesian Regression of Density MapsPreprint

Gomariz, Á., Portenier, T., Nombela‐Arrieta, C., & Göksel, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.11865 · Cited by 1

Epistemic uncertainty estimation for object detection in open-set conditions

Miller, D. (2021) · Queensland University of Technology · doi:10.5204/thesis.eprints.213588 · Cited by 1

Random effects adjustment in machine learning models for cardiac surgery risk prediction: a benchmarking studyPreprint

Dong, T., Sinha, S., Fudulu, D. P., Chan, J., Zhai, B., Narayan, P., Caputo, M., Judge, A., Dimagli, A., Benedetto, U., et al. (2023) · medRxiv · doi:10.1101/2023.06.08.23291129 · Cited by 1

Lost in the Folds: When Cross-Validation Is Not a Deep Ensemble for Uncertainty Estimation

Kirscher, T., Bujotzek, M., Kirchhoff, Y., Rokuss, M., Isensee, F., Kahl, K.-C., Kovacs, B., & Maier-Hein, K. (2026) · arXiv · doi:10.48550/arxiv.2605.18329

Gambling Adversarial Nets for Hard Sample Mining and Structured Prediction: Application in Ultrasound Thyroid Nodule Segmentation

Bakhtiariziabari, M., & Ghafoorian, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_52

An Infrared Image Target Segmentation Based on Improved Threshold Method

Ma, M., Liu, D., & Zhang, R. (2021) · International Journal of Circuits Systems and Signal Processing · doi:10.46300/9106.2021.15.90

Human-Robot Collaborations in Industrial Automation

Schmitz, A. (2022) · MDPI eBooks · doi:10.3390/books978-3-0365-5214-9

Medical Imaging: Artificial Intelligence (AI) and Decision Uncertainty - a Short Survey

Spagnolo, G. S., & Leccese, F. (2022) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine54828.2022.9967587

Pixel-wise confidence estimation for segmentation in Bayesian Convolutional Neural Networks

Martin, R., & Duong, L. (2023) · Machine Vision and Applications · doi:10.1007/s00138-022-01369-9

Novel-domain Object Segmentation via Reliability-aware Teacher Ensemble

Miao, J., Luo, W., Zaidi, N. A., & Wang, J. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00127

Estimating Model Uncertainty in Fluorine-19 MRI Using Stochastic GradientPreprint

Javanbakhat, M., Starke, L., Waiczies, S., & Lippert, C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4492958

Joint Learning Annotator Calibration and Annotator Preference for Multiple Annotations Optic Disc and Cup Segmentation

Guo, X., Shi, P., Lu, S., Ye, C., & Ma, T. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230708

Graph convolutional networks for automated intracranial artery labeling

Vos, I. N., Ruigrok, Y. M., Bhat, I., Timmins, K. M., Velthuis, B. K., & Kuijf, H. J. (2024) · Journal of medical imaging · doi:10.1117/1.jmi.11.1.014007

Image Segmentation Methods for the Intelligent Supervision of Putonghua Exams

Sharma, T., Al‐Azzawi, W. K., Abdulrazzaq, A. Z., & Alrubayi, A. H. (2024) · Data-intensive research · doi:10.1007/978-981-99-9179-2_55

Retaining Informative Latent Variables in Probabilistic Segmentation

Valiuddin, M. M. A., Viviers, C. G. A., van Sloun, R. J. G., de With, P. H. N., & van der Sommen, F. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447580

Functionalized Carbon Nanostructures for Smart Bio-imaging Devices

Moradi, R., Khalili, N. P., & Aliyeva, V. (2024) · Springer eBooks · doi:10.1007/978-3-031-14955-9_65-1

Focused Difficult-to-Classify Pixel Loss Function for Mitigating Class Imbalance in Medical Image SegmentationPreprint

Zhang, W., Chen, Y., Long, Z., Chen, H., Zhang, Y., & Le, X. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4828410

Customized T-time inner sampling network with uncertainty-aware data augmentation strategy for multi-annotated lesion segmentation

Zhou, X., Wang, X., Ma, H., Zhang, J., Wang, X., Bai, X., Zhang, L., Long, J., Chen, J., Le, H., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108990

Semantic feature-based test selection for deep neural networks: A frequency domain perspective

Jiang, Z., Li, H., Tian, X., & Wang, R. (2024) · Computer Science and Information Systems · doi:10.2298/csis230907045j

PDM: A Plug-and-Play Perturbed Multi-path Diffusion Module for Simultaneous Medical Image Segmentation Improvement and Uncertainty Estimation

Zhou, B., Chen, T., Hou, J. Y., Zhou, Y., Xie, H., Liu, C., & Duncan, J. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73284-3_26

Quantifying Uncertainty in Economics Policy Predictions: A Bayesian & Monte Carlo based Data-Driven ApproachPreprint

Rahaman, S. U., & Abdul, M. J. (2024) · Research Square · doi:10.21203/rs.3.rs-5299835/v1

Mask-TS Net: Mask Temperature Scaling Uncertainty Calibration for Polyp Segmentation

Zhang, Y., Xu, C., Xu, K., & Zhu, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78128-5_12

From out-of-distribution detection to quality control

Lambert, B., Forbes, F., & Michel Dojat, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00014-6 · Cited by 1

Predicting Calibrated Conversion Rate of Online Advertising Using a Multi-task Mixture-of-Experts Calibration Model

Zhang, X., Guo, Y., & Ao, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1024-2_14

Divergent Ensemble Networks: Enhancing Uncertainty Estimation with Shared Representations and Independent BranchingPreprint

Kharbanda, A., & Chandorkar, A. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5065471

Uncertainty-aware Mueller matrix polarimetry using Gaussian process pipeline

Robinson, D., Kleijn, W. B., Novikova, T., & Doronin, A. A. (2025) · Polarized Light and Optical Angular Momentum for Biomedical Diagnostics · doi:10.1117/12.3043896

Melanoma Detection by CNN Model with Optimal Rough Set Approximation-based Similarity Measure Classification

Selvi, K., & Shalome, A. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.237

Confidence-Aware Superpixel Self-Supervised Learning for Cross-Modal Point Cloud Recognition

Wang, Y., Zhang, B., Wan, Y., Yue, Z., & Zhang, Y. (2025) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2025.3587236

Deep bioinspired evolutionary stacking algorithm for unpaired multimodal cell classification calibration

Zhao, L., Xu, D., Tan, X., Yang, J., Ding, W., Zhu, H., Zhang, L., & Wang, Q. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112153

ColorEM-Net: Automated Segmentation of Structures in Large-Scale Electron Microscopy Using Element-Derived Ground Truth

Aswath, A., Alsahaf, A. M. J., Duinkerken, B. H. P., Hoogenboom, J. P., Giepmans, B. N. G., & Azzopardi, G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04968-1_19

Deep neural network-based image segmentation system to create slap-optimized sequence

Jansirani, M., & Sumitra, P. (2025) · OPSEARCH · doi:10.1007/s12597-025-01032-7

Artificial intelligence in cancer imaging

Fang, M., Dong, D., Hui, H., Liu, Z., An, Y., Wang, S., He, B., Shao, L., & Tian, J. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-22253-5.00020-0

Radiomics-based intelligent diagnosis and treatment

Fang, M., Wang, Z., Fu, J., Wang, Y., Zhao, Y., Qin, Z., Zhang, C., Xie, X., Tang, L., & Dong, D. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30142-1.00006-7

A Systematic Mapping Study on Brain Tumor Segmentation Based on Machine Learning Algorithms

Murad, S. H., & Ahmed, H. M. (2025) · Emerging technologies and engineering journal. · doi:10.53898/etej2025221

Uncertainty-Guided Prototype Mixture Model for Skin Lesion Segmentation

Li, W., Wang, S., Chen, N., Lu, R., & Shi, X. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229213

Improving automated prostate pathological grading via confidence filteringPreprint

Fogarty, R., Goldgof, D. B., Hall, L., Dhillon, J., Chumbalkar, V. C., & Balagurunathan, Y. (2025) · medRxiv · doi:10.1101/2025.11.25.25340482

3-D TEE Mitral Valve Segmentation and Mesh Reconstruction with Real-Time Quality Assurance

Huynh, P. K., Kpodonu, J., Minh, L. H. N., Nguyen, D. D., Huynh, P., Thuan, P. Q., Olaniran, O. H., Tran, T., Rutledge-Jukes, H., Xu, Y., et al. (2025) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi67747.2025.11269550

Weakly-Supervised Chromosome Segmentation via SAM and Uncertainty-Guided Pseudo-Labeling

Umbreen, N., Ali, S., Sajid, H., Iqbal, K. F., Ayaz, Y., Fayyaz, M. A. B., & Sial, M. B. (2025) · International Conference on Biomedical Signal and Image Processing (ICBIP) · doi:10.1109/icbip66535.2025.11284451

Gestalt-Inspired Feature Integration Network With Entropy Uncertainty Modeling for Pathology Image Segmentation

Fan, D., Wen, J., Dong, H., Han, M., Li, J., Cai, C., Chen, Y., Yang, C., Chen, R., & Wei, L. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3645354

Uncertainty Quantification for Metaplasia Detection in Gastrointestinal Histopathology Images

Acosta-Velasquez, R., Cano, F., Romero, E., & Gonzalez, F. A. (2025) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim67325.2025.11283194

A Multi-Level Probabilistic Deep Learning Network Augmented With Normalizing Flow for Ambiguous Medical Image Segmentation

Shyam, S. P., Fattah, S. A., & Saquib, M. (2025) · IEEE Access · doi:10.1109/access.2025.3650056

Dynamic target recognition and obstacle avoidance in robot vision system based on YOLOv4 algorithm

Gao, W., & Liu, M. (2026) · Australian Journal of Electrical & Electronics Engineering · doi:10.1080/1448837x.2025.2593689

InterHDC: Interactive hierarchical discrepancy compensation modeling for thyroid ultrasound segmentation

Wang, T., Jin, C., Liu, T., Shi, B., Zhou, G., Ge, R., Coatrieux, J.-L., Tang, H., Chen, Y., Feng, Q., et al. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111206

Uncertainty-Aware Brain Tumor Segmentation

Pal, R., Kumar, S., & Bhatnagar, G. (2025) · IEEE International Conference on Computer Vision and Machine Intelligence (CVMI) · doi:10.1109/cvmi66673.2025.11337653

UACNet: Uncertainty-aware network with opinion conjunction and evidence-driven channel attention for medical image segmentation

Wang, J., Ye, H., Li, S., & Cao, F. (2026) · International Journal of Wavelets Multiresolution and Information Processing · doi:10.1142/s0219691326500049

MA-HybridBTS: Modality-Aware Multi-Scale Hybrid 3D Conv-Transformer for Brain Tumor Segmentation

Wang, J., Miao, S., Qin, N., Chen, X., Chen, X., & Zhu, L. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356130

A novel deep learning approach for mosquito species classification via a dual-head structure and calibration-aware fusion architecture

Nazari, M. Z., Zarchi, M. S., Emadi, S., & Poormohammadi, H. (2026) · Scientific Reports · doi:10.1038/s41598-026-35453-1 · Cited by 1

Adaptive Reward Weighting via Reinforcement Learning for Major Depressive Disorder Diagnosis

Hwang, J.-Y., Ji, C.-H., Oh, J.-H., Kang, Y.-K., Kwak, S.-Y., Han, J.-W., & Kam, T. (2026) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic69189.2026.11385961

Calibrated test-time prompt tuning for vision-language models on medicine via information theoretic

ge, C., Li, Y., & Song, G. (2026) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02905-5

Uncertainty-Aware Hybrid Self-Supervised Multi-Modal Framework for Robust Glioma Detection and Classification

A.Devi, Subadra, K. G., Shree Varshan, B., Ajay, S. S., Dharanyadevi, P., & A.Karthik (2025) · International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT) · doi:10.1109/iconscept66142.2025.11437078

MR Imaging Segmentation and Uncertainty Quantification: A Review

Semwal, S., Murmu, G., & Singh, A. K. (2025) · International Conference on Electrical, Electronics, Communication and Computers (ELEXCOM) · doi:10.1109/elexcom67950.2025.11451628

Self-supervised domain adaptive cervical lesion segmentation with cross scale attention

Raja Rajeswari, B., & Karthikumar, S. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110172

Mind the Gap: Human and AI Uncertainties in Cardiac MRI Segmentation

Lin, Q., Chen, X., Chen, C., Jathanna, N., Swoboda, P., Jamil‐Copley, S., & Garibaldi, J. M. (2026) · Cardiovascular Engineering and Technology · doi:10.1007/s13239-026-00832-2

Dealing with confusing samples in image classification based model

Xie, J. (2022) · theses.fr (ABES) · doi:10.70675/fa6adbdaz865ez4e50za916z02e04f274eaf

Uncertainty-based Deep Learning Methods for Robust and Reliable Cardiac MRI Segmentation and Analysis

Arega, T. W. (2023) · theses.fr (ABES) · doi:10.70675/6f691b31z766dz4a4azb504za7b14d4cf213

Medical image segmentation with belief function theory and deep learning

Huang, L. (2023) · doi:10.70675/8a8a9e2bz03f7z4d78z879cz9b80ab963e96

Trusted polyp segmentation via multi-scale evidence ensemble in colonoscopy images

Chen, M., Cao, B., Zhao, J., & Li, J. (2026) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-026-03635-x

Radiomics Applicability Domain Analysis Classification Framework (RADAN-CF): A method for evaluating prediction reliability in radiomics

Rodríguez-Belenguer, P., Marfil-Trujillo, M., Vraka, A., Tsiknakis, M., Papanikolaou, N., Regge, D., Kostas Marias, A., Alberich, L. C., & Martí‐Bonmatí, L. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109375

KG-SAM: Encoding Structural Constraints into Segment Anything Models Via Probabilistic Graphical Models

Li, Y., Chang, D., & Xiao, X. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460706 · Cited by 1

A causality-guided lightweight model for 3D multi-modal brain tumor segmentation

Fan, Y., Wang, C., Zhang, X., Yue, Z., Chen, J., & Zhou, Q. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110392

Improving visual corrosion measurement accuracy via spectral-structural distribution alignment in deep neural networks

Espinoza, J. A., Parvez, M. R., Rojas, D., & Garcês, H. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6a0b

Deep learning-assisted assessment of tooth ankylosis on CBCT - derived multi-view 2D images: a retrospective studyPreprint

Lu, Z., Wu, G., Du, T., Hu, Z., Ba, H., Wang, S., & Guo, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-9274278/v1

Segmentation Confidence for Arbitrary CNNS

Oguz, B. U., Yao, X., Tawil, C., Pouch, A. M., Byram, B. C., Arenas, G. A., Schwartz, N., & Oğuz, İ. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515329

A Hybrid Diversity-Aware Active Learning Framework for Efficient Histopathology Image Segmentation

Deotale, G., Das, D. K., Ramchandani, L., & Thomas, T. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515357

Uncertainty-guided model learning for trustworthy medical image segmentation

Ye, Q., Guo, L., & Wu, Q. (2026) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-026-03591-3

CG-IRNet: Structure–Confidence Hybrid Learning for Low-False-Alarm Infrared Small Target Detection

Zhu, Z., & Liao, M. (2026) · Electronics · doi:10.3390/electronics15112405

Uncertainty aware pre-stack seismic inversion using probabilistic neural networks

Morik, M., Talarico, E., Sobreira, M. C. A., de Lima, L. A. B., & Nakajima, S. (2026) · Franklin Open · doi:10.1016/j.fraope.2026.100652

Identification of Nitinol Robotic Needles in Magnetic Resonance Imaging via Simulation-Driven Deep Learning

Chen, H., Qi, B., Rezaeian, S., Akbari, A., Langley, J. A., Hu, X., & Sheng, J. (2026) · International Symposium on Medical Robotics · doi:10.1109/ismr69606.2026.11536195

Dynamic uncertainty-level assessment framework for real-time needle tracking in CT-guided surgical environments

Steiger, M., Rezapourian, M., Rak, M., & Hansen, C. (2026) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-026-03725-w

Lungawarenet: Attention-Guided Dual-View Transformer Framework for Uncertaintyaware Pneumonia Detection

Patel, U., Raval, D., & Soni, K. G. (2026) · International Conference on Signal, Systems, and Computing for Next-Gen Automation (ICSSCNA) · doi:10.1109/icsscna68616.2026.11546998

Uncertainty-aware Bayesian prompt adaptation of SAM2 for few-shot cross-modal segmentation

Hong, S., Yoon, J. P., Lee, K., & Kim, J.-Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133226

Parameter-Efficient Adaptation of Vision Transformers for Retinal OCT Classification

Ahmed, M. S., & Ahmed, F. (2026) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain69676.2026.11545861

Modeling Aleatoric Uncertainty in Cardiac MRI Segmentation: Probabilistic Detection and Contour Regression

Zhao, Y., Zhang, Y., Tourais, J., Weingärtner, S., Suinesiaputra, A., Young, A. A., Han, Y., Simonetti, O., & Tao, Q. (2026) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2026.3702822

DA-Cal: Toward Cross-Domain Calibration in Semantic Segmentation

Li, W., Sun, R., Li, Z., Chen, Y., & Zhang, T. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3705209

Prediction Uncertainty Guided Feature Modulation for Polyp Image Segmentation

Yu, Y. (2026) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis70043.2026.11572007

ChemoCardioNet: An Explainable Multimodal Transformer for Early Prediction of Chemotherapy-Induced Cardiotoxicity

Dakua, S. P. (2026) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-026-10807-2

OncoSeg2D: A deep framework for semantic segmentation of lung cancer in 2D CT scans

Yu, L., Dong, X., 法凯, & Li, Y. (2026) · PLoS ONE · doi:10.1371/journal.pone.0348719

Automated Craniofacial Artery Segmentation with Vessel Enhancement-Guided Deep Learning

Park, H., Kim, Y. W., Koo, K., Kang, S., Choi, J. W., & Park, C.-U. (2026) · Bioengineering · doi:10.3390/bioengineering13070728

Mitigating measurement-induced training instability in hybrid quantum neural networks for protein classification

Mondal, M., Chanda, S., Alawieh, M. M., Sukhadiya, B., Krah, D., Gonsalves, C., Ntolkeras, A., Rizzoli, S. O., & Shaib, A. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115548

Uncertainty Quantification for Trustworthy Breast Cancer CAD in Mammography: A Critical Review and Research Agenda

Lbachir, I. A., Daoudi, I., & Nassih, R. (2026) · International Conference on Intelligent Systems and Digital Applications (ISDA) · doi:10.1109/isda70544.2026.11605993

Uncertainty quantification for artificial intelligence in medical imaging: what every radiologist needs to know

Vega, F., Koopmans, L. D., Roest, C., Türkbey, B., Yakar, D., & Kwee, T. C. (2026) · Abdominal Radiology · doi:10.1007/s00261-026-05714-8 · Cited by 1

TopoLoRA-SAM: Topology-Aware Parameter-Efficient Adaptation of Foundation Segmenters for Thin-Structure and Cross-Domain Binary Semantic Segmentation

Khazem, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31397-3_45

The Confidence Trap: Calibration Attacks for Graph Neural Networks

Dang, C., Zhang, J., Quang, H. T., Le, D., Cheng, L., & Wang, S. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3817898

A multi-scale spatiotemporal uncertainty quantification framework for combustion flow-field prediction: Uncertainty masks with GELU-enhanced attention and non-exchangeable conformal calibration

Qian, X., Jin, W., Guo, Z., Tang, C., & Li, J. (2026) · Aerospace Science and Technology · doi:10.1016/j.ast.2026.113474

OOD-aware Reliability Learning for open-world semi-supervised liver lesion recognition

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Towards Reliable Medical Image Segmentation by Modeling Evidential Calibrated UncertaintyPreprint

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