Semi-Supervised Learning

Also known as SSL

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9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Quality-Guided Semi-Supervised Learning for Medical Image Segmentation
Jun 1, 2026Kumar Abhishek, Ghassan HamarnehSemi-Supervised Medical Image Segmentation Medical Image Segmentation
School of Computing Science, Simon Fraser University, Canada
Learning Label-Efficient Interpretable Medical Image Diagnosis via Semi-supervised Hypergraph Concept Bottleneck Model
Jun 1, 2026Yijun Yang, Ruiqiang Xiao, Lijie Hu +4Concept Bottleneck Models Medical Image Classification
HKUST(GZ) · Joy Future Academy · MBZUAI +3
Semi-Supervised Hyperbolic Hierarchical Clustering with Set-Level Structural Priors
Jun 1, 2026Junjing Zheng, Xinyu Zhang, Xiangfeng Qiu +2Clustering Hierarchical Clustering
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
May 30, 2026Yuan Yao, Jin Song, Huixia Li +3Domain Adaptation Transfer Learning
Teleinfo, CAICT · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) · Nanjing University of Posts and Telecommunications +4
Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
May 30, 2026Kwangho Kim, Jisu KimKernel Ridge Regression Semi-Supervised Learning
Department of Statistics, Korea University, Seoul, Korea · Department of Statistics, Seoul National University, Seoul, Korea
Semi-Supervised Hypothesis Testing by Betting on Predictions
May 27, 2026Yaniv Tenzer, Elad Tolochinsky, Yaniv RomanoE-Values Semi-Supervised Learning
Department of Computer Science, Technion – Israel Institute of Technology · Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology
Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation
May 27, 2026Runlong Cao, Ying Zang, Chuanwei Zhou +4Referring Expression Segmentation Pseudo-Labeling
School of Computer Science and Engineering, Nanjing University of Science and Technology · School of Information Engineering, Huzhou Normal University · School of Artificial Intelligence, Nanjing University of Posts and Telecommunications +3
DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification
May 27, 2026Shubhashis Roy Dipta, Ankur Padia, Francis FerraroClaim Verification RL for Language Model Reasoning
Department of Computer Science and Electrical Engineering · University of Maryland Baltimore County · Baltimore, MD 21250 USA
Semi-Supervised Gaze Estimation via Disentangled Subspace Contrastive Learning
May 26, 2026Qida Tan, Hongyu Yang, Wenchao DuContrastive Learning Gaze Estimation
National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China · College of Computer Science, Sichuan University, Chengdu, China
SCKAN: Structural Consensus-based KAN Prototype Learning for Semi-Supervised Pancreas Segmentation
May 26, 2026Yuqi Liu, Yufei Chen, Wei Fu +2Prototype Learning Medical Image Segmentation
School of Computer Science and Technology, Tongji University, Shanghai, China · Artificial Intelligence Institute, Shanghai University, Shanghai, China · Department of Computer and Data Science and Department of Biomedical2026 Engineering, Case Western Reserve University, Cleveland, USA
ARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification
May 25, 2026VSS Tejaswi Abburi, Saurabh J. Shigwan, Nitin KumarGraph Neural Networks Unsupervised Representation Learning
Department of Computer Science and Engineering, Shiv Nadar Institute of Eminence Deemed to be University, Delhi NCR, India
Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?
May 25, 2026Jun Li, Ziwei QinBenchmark Contamination Semi-Supervised Medical Image Segmentation
Institute of Systems Science and Technology, School of Electrical Engineering, Southwest Jiaotong University, China.
Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning
May 24, 2026Ziheng Cheng, Yixiao Huang, Hanlin Zhu +5Diffusion Policy Semi-Supervised Learning
University of California, Berkeley.
SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget
May 24, 2026Suresh Kumar Amalapuram, Bikraj Shresta, Siva Ram murthy Chebiyam +2Concept Drift Continual Learning
Indian Institute of Technology Ropar, India · Indian Institute of Technology Hyderabad, India
FDDet: Achieving Data-Efficient Food Defect Detection Under Real-World Scenarios
May 23, 2026Ruihao Xu, Yong Liu, Yansong TangObject Detection Semi-Supervised Learning
Tsinghua Shenzhen International Graduate School, Tsinghua University
Music Transcription with (Almost) No Supervision
May 22, 2026Saebyeol Shin, Chao Wan, Zhenzhen Liu +4Unsupervised Domain Adaptation Automatic Music Transcription
Cornell University, Ithaca, NY
Coupled Training with Privileged Information and Unlabeled Data
May 22, 2026Jiahao Shi, Omar Hagrass, Jason M. KlusowskiLearning Using Privileged Information Semi-Supervised Learning
Department of Electrical and Computer Engineering, Princeton University · Department of Operations Research and Financial Engineering, Princeton University
Synergistic Foundation Models for Semi-Supervised Fetal Cardiac Ultrasound Analysis: SAM-Med2D Boundary Refinement and DINOv3 Semantic Enhancement
May 19, 2026Tonghao Zhuang, Shanglong Hu, Yongsheng Luo +2Ultrasound Image Segmentation Medical Imaging Foundation Models
Zhuhai College of Science and Technology, Zhuhai, China
An Approximation Algorithm for Graph Label Selection
May 18, 2026Josia John, Simon Meierhans, Maximilian Probst GutenbergSemi-Supervised Learning Approximation Algorithms
Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Semi-LAR: Semi-supervised Contrastive Learning with Linear Attention for Removal of Nighttime Flares
May 18, 2026Xiyu Zhu, Wei Wang, Kui Jiang +1Contrastive Learning Image Restoration
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China · Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China · SRO department, Institute for Infocomm Research, A*STAR, Singapore.
Graph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
May 18, 2026Brown Zaz, Mar Gonzàlez I Català, Ferran Hernandez Caralt +2Graph Neural Networks Graph-Based Semi-Supervised Learning
University of Cambridge
Multi-task learning on partially labeled datasets via invariant/equivariant semi-supervised learning
May 17, 2026Miquel Martí i Rabadán, Alessandro Pieropan, Hossein Azizpour +1Multi-Task Learning Object Detection
Univrses AB, Stockholm, Sweden · KTH Royal Institute of Technology, Stockholm, Sweden
Collaborative Learning for Semi-Supervised LiDAR Semantic Segmentation
May 16, 2026Bin Yang, Alexandru Paul Condurache3D Semantic Segmentation Semi-Supervised Learning
Bosch Research, Robert Bosch GmbH, Stuttgart, Germany · Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
Bidirectional Fusion Guided by Cardiac Patterns for Semi-Supervised ECG Segmentation
May 15, 2026Jeonghwa Lim, Minje Park, Sunghoon JooSynthetic Data Augmentation Electrocardiography
VUNO Inc., Seoul, South Korea
Avoiding Structural Failure Modes in Tabular Fair SSL: Online Primal-Dual Allocation under Confidence Gating
May 15, 2026Hangchuan Liang, Changchun LiPrimal-Dual Optimization Group Fairness
College of Computer Science and Technology, Jilin University, China · Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Jilin University, China
A Semi-Supervised Framework for Speech Confidence Detection using Whisper
May 12, 2026Adam Wynn, Jingyun WangAudio Understanding Pseudo-Labeling
Department of Computer Science, Durham, United Kingdom, DH1 3DF
Relational Retrieval: Leveraging Known-Novel Interactions for Generalized Category Discovery
May 10, 2026Yulin Xu, Chunqi Guo, Yuanzhen Shuai +1Semi-Supervised Learning Generalized Category Discovery
University College London London, UK · Juniata College Huntingdon, USA
Semi-Supervised Neural Super-Resolution for Mesh-Based Simulations
May 10, 2026Jiyeon Kim, Youngjoon Hong, Won-Yong ShinSemi-Supervised Learning Data-Efficient Learning
School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, Seoul, Republic of Korea · Department of Mathematical Sciences, Seoul National University, Seoul, Republic of Korea
SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data
May 8, 2026Kacper Jurek, Wojciech Batko, Marek Śmieja +1Multi-View Learning Few-Shot Learning
Jagiellonian University, Faculty of Mathematics and Computer Science · AGH University of Krakow
LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
May 8, 2026Jacob Ativo, Bharaneeshwar Balasubramaniyam, Anh Tran +4Social Media Analysis Semi-Supervised Learning
Department of Computer Science University of Illinois at Chicago

Jun 1, 2026, cs.CVJ/K move · Enter open · S save

Quality-Guided Semi-Supervised Learning for Medical Image Segmentation

Kumar Abhishek, Ghassan Hamarneh

School of Computing Science, Simon Fraser University, Canada

Semi-Supervised Medical Image Segmentation Medical Image Segmentation Pseudo-Labeling Semi-Supervised Learning

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Training accurate medical image segmentation models requires large amounts of densely annotated data, which is costly and time-consuming to obtain. Semi-supervised learning (SSL) alleviates this by learning from both abundant unlabeled data and limited labeled data. However, most modern SSL methods rely on pseudolabels for unlabeled data, and typically assess their reliability through model confidence or uncertainty, measures that are self-referential and lack explicit grounding in segmentation quality. Instead, we propose a quality-guided SSL framework that trains a dedicated network to estimate segmentation quality from image-mask pairs. The predictor is trained on variable-quality masks generated through synthetic corruptions augmented with imperfect outputs from partially trained segmentation models, capturing realistic error patterns encountered during training. We integrate the quality predictor into SSL through two complementary mechanisms: a quality-aware regularization loss and a quality-based pseudolabel sample reweighting scheme. We show that our method serves as a drop-in enhancement to existing SSL frameworks. Extensive experiments across five datasets and multiple architectures demonstrate consistent improvements over competing SSL methods, advancing the state-of-the-art in semi-supervised medical image segmentation.

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Latest papers 127