{"total":118,"rows":[{"id":"2211.14830","title":"Medical Image Segmentation Review: The success of U-Net","created":"2022-11-27","cited_by_count":191,"area":null},{"id":"2206.02066","title":"PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers","created":"2022-06-04","cited_by_count":84,"area":null},{"id":"2201.00443","title":"Scene Graph Generation: A Comprehensive Survey","created":"2022-01-03","cited_by_count":73,"area":null},{"id":"2207.08549","title":"Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentation","created":"2022-07-18","cited_by_count":65,"area":null},{"id":"2202.05273","title":"Towards a Guideline for Evaluation Metrics in Medical Image Segmentation","created":"2022-02-10","cited_by_count":60,"area":null},{"id":"2104.05642","title":"Common Limitations of Image Processing Metrics: A Picture Story","created":"2021-04-12","cited_by_count":59,"area":null},{"id":"2302.01790","title":"Understanding metric-related pitfalls in image analysis validation","created":"2023-02-03","cited_by_count":58,"area":null},{"id":"2111.12940","title":"Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation","created":"2021-11-25","cited_by_count":57,"area":null},{"id":"2305.05803","title":"Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation","created":"2023-05-09","cited_by_count":53,"area":null},{"id":"2210.04388","title":"Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization","created":"2022-10-10","cited_by_count":50,"area":null},{"id":"2102.13645","title":"Convolution-Free Medical Image Segmentation using Transformers","created":"2021-02-26","cited_by_count":49,"area":null},{"id":"2301.13366","title":"CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects","created":"2023-01-31","cited_by_count":43,"area":null},{"id":"2312.05391","title":"Loss Functions in the Era of Semantic Segmentation: A Survey and Outlook","created":"2023-12-08","cited_by_count":34,"area":null},{"id":"2306.04557","title":"PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain","created":"2023-06-07","cited_by_count":31,"area":null},{"id":"2312.00634","title":"A Recent Survey of Vision Transformers for Medical Image Segmentation","created":"2023-12-01","cited_by_count":23,"area":null},{"id":"2103.00429","title":"Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models","created":"2021-02-28","cited_by_count":22,"area":null},{"id":"2206.00356","title":"A Survey on Deep Learning for Skin Lesion Segmentation","created":"2022-06-01","cited_by_count":22,"area":null},{"id":"2301.08160","title":"FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Network","created":"2023-01-19","cited_by_count":22,"area":null},{"id":"2308.05305","title":"From CNN to Transformer: A Review of Medical Image Segmentation Models","created":"2023-08-10","cited_by_count":22,"area":null},{"id":"2107.04537","title":"Modality specific U-Net variants for biomedical image segmentation: A survey","created":"2021-07-09","cited_by_count":20,"area":null},{"id":"2101.07721","title":"A survey on shape-constraint deep learning for medical image segmentation","created":"2021-01-19","cited_by_count":16,"area":null},{"id":"2112.04665","title":"Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation","created":"2021-12-09","cited_by_count":15,"area":null},{"id":"2305.19949","title":"Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation","created":"2023-05-31","cited_by_count":15,"area":null},{"id":"2111.11629","title":"Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation","created":"2021-11-23","cited_by_count":12,"area":null},{"id":"2403.06488","title":"Query-guided Prototype Evolution Network for Few-Shot Segmentation","created":"2024-03-11","cited_by_count":12,"area":null},{"id":"2407.18070","title":"CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image Segmentation","created":"2024-07-25","cited_by_count":12,"area":null},{"id":"2405.01636","title":"Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey","created":"2024-05-02","cited_by_count":11,"area":null},{"id":"2409.04758","title":"SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance","created":"2024-09-07","cited_by_count":11,"area":null},{"id":"2307.10822","title":"Gradient-Semantic Compensation for Incremental Semantic Segmentation","created":"2023-07-20","cited_by_count":10,"area":null},{"id":"2405.01725","title":"Development of Skip Connection in Deep Neural Networks for Computer Vision and Medical Image Analysis: A Survey","created":"2024-05-02","cited_by_count":10,"area":null},{"id":"2109.10702","title":"A Quantitative Comparison of Epistemic Uncertainty Maps Applied to Multi-Class Segmentation","created":"2021-09-22","cited_by_count":9,"area":null},{"id":"2307.02100","title":"MDViT: Multi-domain Vision Transformer for Small Medical Image Segmentation Datasets","created":"2023-07-05","cited_by_count":9,"area":null},{"id":"2206.04879","title":"Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion","created":"2022-06-10","cited_by_count":8,"area":null},{"id":"2205.04227","title":"Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference","created":"2022-05-06","cited_by_count":7,"area":null},{"id":"2301.13361","title":"Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic Segmentation","created":"2023-01-31","cited_by_count":7,"area":null},{"id":"2401.14497","title":"Investigating the Quality of DermaMNIST and Fitzpatrick17k Dermatological Image Datasets","created":"2024-01-25","cited_by_count":7,"area":null},{"id":"2407.09047","title":"Cs2K: Class-specific and Class-shared Knowledge Guidance for Incremental Semantic Segmentation","created":"2024-07-12","cited_by_count":7,"area":null},{"id":"2305.11686","title":"Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs Towards Robot-assisted Intubation","created":"2023-05-19","cited_by_count":6,"area":null},{"id":"2312.16222","title":"Segment Any Events via Weighted Adaptation of Pivotal Tokens","created":"2023-12-24","cited_by_count":5,"area":null},{"id":"2505.01854","title":"Accelerating Volumetric Medical Image Annotation via Short-Long Memory SAM 2","created":"2025-05-03","cited_by_count":5,"area":null},{"id":"2110.12939","title":"Interactive Segmentation via Deep Learning and B-Spline Explicit Active Surfaces","created":"2021-10-25","cited_by_count":4,"area":null},{"id":"2112.06693","title":"Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labels","created":"2021-12-13","cited_by_count":4,"area":null},{"id":"2202.10971","title":"Improving Classification Model Performance on Chest X-Rays through Lung Segmentation","created":"2022-02-22","cited_by_count":4,"area":null},{"id":"2203.05847","title":"Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology","created":"2022-03-11","cited_by_count":4,"area":null},{"id":"2208.02483","title":"Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards","created":"2022-08-04","cited_by_count":4,"area":null},{"id":"2303.04506","title":"Radio astronomical images object detection and segmentation: A benchmark on deep learning methods","created":"2023-03-08","cited_by_count":4,"area":null},{"id":"2304.06672","title":"LSFSL: Leveraging Shape Information in Few-shot Learning","created":"2023-04-13","cited_by_count":4,"area":null},{"id":"2305.12621","title":"DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images","created":"2023-05-22","cited_by_count":4,"area":null},{"id":"2308.06296","title":"Classification of White Blood Cells Using Machine and Deep Learning Models: A Systematic Review","created":"2023-08-11","cited_by_count":4,"area":null},{"id":"2206.02070","title":"Priors in Deep Image Restoration and Enhancement: A Survey","created":"2022-06-04","cited_by_count":3,"area":null},{"id":"2209.10700","title":"Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images","created":"2022-09-21","cited_by_count":3,"area":null},{"id":"2210.01891","title":"Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift","created":"2022-10-04","cited_by_count":3,"area":null},{"id":"2312.01835","title":"Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation","created":"2023-12-04","cited_by_count":3,"area":null},{"id":"2312.07342","title":"Expand-and-Quantize: Unsupervised Semantic Segmentation Using High-Dimensional Space and Product Quantization","created":"2023-12-12","cited_by_count":3,"area":null},{"id":"2408.05699","title":"MacFormer: Semantic Segmentation with Fine Object Boundaries","created":"2024-08-11","cited_by_count":3,"area":null},{"id":"2409.05697","title":"Segmentation by Factorization: Unsupervised Semantic Segmentation for Pathology by Factorizing Foundation Model Features","created":"2024-09-09","cited_by_count":3,"area":null},{"id":"2503.20685","title":"Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound","created":"2025-03-26","cited_by_count":3,"area":null},{"id":"2508.17524","title":"OmniMRI: A Unified Vision--Language Foundation Model for Generalist MRI Interpretation","created":"2025-08-24","cited_by_count":3,"area":null},{"id":"2301.07634","title":"Training Semantic Segmentation on Heterogeneous Datasets","created":"2023-01-18","cited_by_count":2,"area":null},{"id":"2307.10097","title":"Boundary-Refined Prototype Generation: A General End-to-End Paradigm for Semi-Supervised Semantic Segmentation","created":"2023-07-19","cited_by_count":2,"area":null},{"id":"2311.14146","title":"Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning","created":"2023-11-23","cited_by_count":2,"area":null},{"id":"2407.21266","title":"DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs","created":"2024-07-31","cited_by_count":2,"area":null},{"id":"2408.02787","title":"Segmentation Style Discovery: Application to Skin Lesion Images","created":"2024-08-05","cited_by_count":2,"area":null},{"id":"2408.03393","title":"Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods","created":"2024-08-06","cited_by_count":2,"area":null},{"id":"2506.03938","title":"FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review","created":"2025-06-04","cited_by_count":2,"area":null},{"id":"2507.14596","title":"DiSCO-3D : Discovering and segmenting Sub-Concepts from Open-vocabulary queries in NeRF","created":"2025-07-19","cited_by_count":2,"area":null},{"id":"2507.22361","title":"Object Recognition Datasets and Challenges: A Review","created":"2025-07-30","cited_by_count":2,"area":null},{"id":"2508.09381","title":"What Can We Learn from Inter-Annotator Variability in Skin Lesion Segmentation?","created":"2025-08-12","cited_by_count":2,"area":null},{"id":"2509.22399","title":"Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks","created":"2025-09-26","cited_by_count":2,"area":null},{"id":"2103.02220","title":"Unsupervised Domain Adaptation Network with Category-Centric Prototype Aligner for Biomedical Image Segmentation","created":"2021-03-03","cited_by_count":1,"area":null},{"id":"2106.00389","title":"Analysis of Vision-based Abnormal Red Blood Cell Classification","created":"2021-06-01","cited_by_count":1,"area":null},{"id":"2106.09201","title":"Trilateral Attention Network for Real-time Medical Image Segmentation","created":"2021-06-17","cited_by_count":1,"area":null},{"id":"2201.07931","title":"Experimental Large-Scale Jet Flames' Geometrical Features Extraction for Risk Management Using Infrared Images and Deep Learning Segmentation Methods","created":"2022-01-20","cited_by_count":1,"area":null},{"id":"2201.07935","title":"Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images","created":"2022-01-17","cited_by_count":1,"area":null},{"id":"2204.03323","title":"Multi-Sample $\\zeta$-mixup: Richer, More Realistic Synthetic Samples from a $p$-Series Interpolant","created":"2022-04-07","cited_by_count":1,"area":null},{"id":"2204.05276","title":"Segmentation-Consistent Probabilistic Lesion Counting","created":"2022-04-11","cited_by_count":1,"area":null},{"id":"2302.04135","title":"Multi-Modal Evaluation Approach for Medical Image Segmentation","created":"2023-02-08","cited_by_count":1,"area":null},{"id":"2303.04418","title":"FUSQA: Fetal Ultrasound Segmentation Quality Assessment","created":"2023-03-08","cited_by_count":1,"area":null},{"id":"2305.15608","title":"Semantic Segmentation by Semantic Proportions","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2310.07149","title":"Robust Unsupervised Domain Adaptation by Retaining Confident Entropy via Edge Concatenation","created":"2023-10-11","cited_by_count":1,"area":null},{"id":"2312.05407","title":"ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation","created":"2023-12-08","cited_by_count":1,"area":null},{"id":"2403.08042","title":"CT evaluation of 2D and 3D holistic deep learning methods for the volumetric segmentation of airway lesions","created":"2024-03-12","cited_by_count":1,"area":null},{"id":"2406.13750","title":"Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks","created":"2024-06-19","cited_by_count":1,"area":null},{"id":"2409.06605","title":"Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer","created":"2024-09-10","cited_by_count":1,"area":null},{"id":"2411.01669","title":"MamT$^4$: Multi-view Attention Networks for Mammography Cancer Classification","created":"2024-11-03","cited_by_count":1,"area":null},{"id":"2502.02340","title":"Transfer Risk Map: Mitigating Pixel-level Negative Transfer in Medical Segmentation","created":"2025-02-04","cited_by_count":1,"area":null},{"id":"2503.02053","title":"EPEE: Towards Efficient and Effective Foundation Models in Biomedicine","created":"2025-03-03","cited_by_count":1,"area":null},{"id":"2505.19659","title":"LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation","created":"2025-05-26","cited_by_count":1,"area":null},{"id":"2506.10675","title":"ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation","created":"2025-06-12","cited_by_count":1,"area":null},{"id":"2506.14265","title":"Self-supervised Representation Learning with Local Aggregation for Image-based Profiling","created":"2025-06-17","cited_by_count":1,"area":null},{"id":"2507.13061","title":"Advancing Complex Wide-Area Scene Understanding with Hierarchical Coresets Selection","created":"2025-07-17","cited_by_count":1,"area":null},{"id":"2107.02476","title":"A new smart-cropping pipeline for prostate segmentation using deep learning networks","created":"2021-07-06","cited_by_count":0,"area":null},{"id":"2108.07368","title":"CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects","created":"2021-08-16","cited_by_count":0,"area":null},{"id":"2111.03388","title":"A Deep Learning Generative Model Approach for Image Synthesis of Plant Leaves","created":"2021-11-05","cited_by_count":0,"area":null},{"id":"2201.08816","title":"Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation","created":"2022-01-14","cited_by_count":0,"area":null},{"id":"2202.06465","title":"A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification","created":"2022-02-14","cited_by_count":0,"area":null},{"id":"2205.09842","title":"Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks","created":"2022-05-19","cited_by_count":0,"area":null},{"id":"2210.12361","title":"MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images","created":"2022-10-22","cited_by_count":0,"area":null},{"id":"2302.01813","title":"Leveraging weak complementary labels to improve semantic segmentation of hepatocellular carcinoma and cholangiocarcinoma in H&E-stained slides","created":"2023-02-03","cited_by_count":0,"area":null},{"id":"2303.17956","title":"Ensemble Methods for Multi-Organ Segmentation in CT Series","created":"2023-03-31","cited_by_count":0,"area":null}],"next_cursor":"100"}