Papers Weakly-supervised Learning
“Weakly-supervised Learning” 태그가 달린 논문 613편 · 필터 해제
Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training
Histopathology whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impur…
Active LearningArtifact DetectionDiagnosticMiscellaneous+2MambaEviScrib: Mamba and Evidence-Guided Consistency Enhance CNN Robustness for Scribble-Based Weakly Supervised Ultrasound Image Segmentation
Segmenting anatomical structures and lesions from ultrasound images contributes to disease assessment. Weakly supervised learning (WSL) based on sparse annotation has achieved encouraging performance and demonstrated the…
Image SegmentationMambaSemantic SegmentationWeakly-supervised LearningAgent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysis
Histopathology analysis is the gold standard for medical diagnosis. Accurate classification of whole slide images (WSIs) and region-of-interests (ROIs) localization can assist pathologists in diagnosis. The gigapixel res…
ClassificationDenoisingMedical DiagnosisMultiple Instance Learning+2On the Generalizability of Foundation Models for Crop Type Mapping
Foundation models pre-trained using self-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text generation, and image recognition. The E…
Crop ClassificationCrop Type MappingDisaster ResponseEarth Observation+4Weakly-supervised Camera Localization by Ground-to-satellite Image Registration
The ground-to-satellite image matching/retrieval was initially proposed for city-scale ground camera localization. This work addresses the problem of improving camera pose accuracy by ground-to-satellite image matching a…
Camera LocalizationContrastive LearningImage RegistrationRetrieval+1Interactive Machine Teaching by Labeling Rules and Instances
Weakly supervised learning aims to reduce the cost of labeling data by using expert-designed labeling rules. However, existing methods require experts to design effective rules in a single shot, which is difficult in the…
Active LearningLanguage ModellingWeakly-supervised LearningTheoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags
Learning from label proportions (LLP) is a kind of weakly supervised learning that trains an instance-level classifier from label proportions of bags, which consist of sets of instances without using instance labels. A c…
GPUWeakly-supervised LearningSize Aware Cross-shape Scribble Supervision for Medical Image Segmentation
Scribble supervision, a common form of weakly supervised learning, involves annotating pixels using hand-drawn curve lines, which helps reduce the cost of manual labelling. This technique has been widely used in medical …
Image SegmentationMedical Image SegmentationSemantic SegmentationWeakly-supervised LearningMergeUp-augmented Semi-Weakly Supervised Learning for WSI Classification
Recent advancements in computational pathology and artificial intelligence have significantly improved whole slide image (WSI) classification. However, the gigapixel resolution of WSIs and the scarcity of manual annotati…
ClassificationDiversityMultiple Instance LearningWeakly-supervised LearningA Review of Human-Object Interaction Detection
Human-object interaction (HOI) detection plays a key role in high-level visual understanding, facilitating a deep comprehension of human activities. Specifically, HOI detection aims to locate the humans and objects invol…
Human-Object Interaction DetectionObjectRelationship DetectionWeakly-supervised Learning+1LNQ 2023 challenge: Benchmark of weakly-supervised techniques for mediastinal lymph node quantification
Accurate assessment of lymph node size in 3D CT scans is crucial for cancer staging, therapeutic management, and monitoring treatment response. Existing state-of-the-art segmentation frameworks in medical imaging often r…
SegmentationWeakly-supervised LearningWeakly supervised segmentationClass-aware and Augmentation-free Contrastive Learning from Label Proportion
Learning from Label Proportion (LLP) is a weakly supervised learning scenario in which training data is organized into predefined bags of instances, disclosing only the class label proportions per bag. This paradigm is e…
Contrastive LearningRepresentation LearningWeakly-supervised LearningWhat Matters in Autonomous Driving Anomaly Detection: A Weakly Supervised Horizon
Video anomaly detection (VAD) in autonomous driving scenario is an important task, however it involves several challenges due to the ego-centric views and moving camera. Due to this, it remains largely under-explored. Wh…
Anomaly DetectionAutonomous DrivingVideo Anomaly DetectionWeakly-supervised LearningSentinel-1 SAR Based Weakly Supervised Learning For Tropical Forest Mapping
Tropical forests play an important role in regulating the global carbon cycle and are crucial for maintaining the tropical forest biodiversity. Therefore, there is an urgent need to map the extent of tropical forest ecos…
Weakly-supervised LearningDistilling High Diagnostic Value Patches for Whole Slide Image Classification Using Attention Mechanism
Multiple Instance Learning (MIL) has garnered widespread attention in the field of Whole Slide Image (WSI) classification as it replaces pixel-level manual annotation with diagnostic reports as labels, significantly redu…
Diagnosticimage-classificationImage ClassificationMultiple Instance Learning+1Physics-Informed Weakly Supervised Learning for Interatomic Potentials
Machine learning plays an increasingly important role in computational chemistry and materials science, complementing computationally intensive ab initio and first-principles methods. Despite their utility, machine-learn…
Computational chemistryWeakly-supervised LearningAsyCo: An Asymmetric Dual-task Co-training Model for Partial-label Learning
Partial-Label Learning (PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but…
Partial Label LearningPartially Labeled DatasetsWeakly-supervised LearningAn efficient framework based on large foundation model for cervical cytopathology whole slide image screening
Current cervical cytopathology whole slide image (WSI) screening primarily relies on detection-based approaches, which are limited in performance due to the expense and time-consuming annotation process. Multiple Instanc…
Contrastive LearningMultiple Instance Learningparameter-efficient fine-tuningSelf-Supervised Learning+1On Characterizing and Mitigating Imbalances in Multi-Instance Partial Label Learning
*Multi-Instance Partial Label Learning* (MI-PLL) is a weakly-supervised learning setting encompassing *partial label learning*, *latent structural learning*, and *neurosymbolic learning*. Unlike supervised learning, in M…
Long-tail LearningPartial Label LearningWeakly-supervised LearningDGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) stands as a powerful approach in weakly supervised learning, regularly employed in histological whole slide image (WSI) classification for detecting tumorous lesions. However, existing ma…
DescriptiveDiversityimage-classificationImage Classification+2