Papers Weakly supervised segmentation
“Weakly supervised segmentation” 태그가 달린 논문 147편 · 필터 해제
Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound
Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule…
Multi-agent Reinforcement LearningSegmentationTAGWeakly supervised segmentationWeakly Supervised Segmentation Framework for Thyroid Nodule Based on High-confidence Labels and High-rationality Losses
Weakly supervised segmentation methods can delineate thyroid nodules in ultrasound images efficiently using training data with coarse labels, but suffer from: 1) low-confidence pseudo-labels that follow topological prior…
Image SegmentationSegmentationSemantic SegmentationWeakly supervised segmentationWeakly Supervised Segmentation of Hyper-Reflective Foci with Compact Convolutional Transformers and SAM2
Weakly supervised segmentation has the potential to greatly reduce the annotation effort for training segmentation models for small structures such as hyper-reflective foci (HRF) in optical coherence tomography (OCT). Ho…
Multiple Instance LearningSegmentationWeakly supervised segmentationEAUWSeg: Eliminating annotation uncertainty in weakly-supervised medical image segmentation
Weakly-supervised medical image segmentation is gaining traction as it requires only rough annotations rather than accurate pixel-to-pixel labels, thereby reducing the workload for specialists. Although some progress has…
Image SegmentationMedical Image SegmentationSemantic SegmentationWeakly supervised segmentationODA-GAN: Orthogonal Decoupling Alignment GAN Assisted by Weakly-supervised Learning for Virtual Immunohistochemistry Staining
Recently, virtual staining has emerged as a promising alternative to revolutionize histological staining by digitally generating stains. However, most existing methods suffer from the curse of staining unreality and …
Contrastive LearningGenerative Adversarial NetworkVirtual StainingWeakly-supervised Learning+1Soft Self-labeling and Potts Relaxations for Weakly-supervised Segmentation
We consider weakly supervised segmentation where only a fraction of pixels have ground truth labels (scribbles) and focus on a self-labeling approach optimizing relaxations of the standard unsupervised CRF/Potts loss…
Weakly supervised segmentationPrototype-Based Image Prompting for Weakly Supervised Histopathological Image Segmentation
Weakly supervised image segmentation with image-level labels has drawn attention due to the high cost of pixel-level annotations. Traditional methods using Class Activation Maps (CAMs) often highlight only the most d…
Contrastive LearningImage SegmentationSegmentationSemantic Segmentation+1HELPNet: Hierarchical Perturbations Consistency and Entropy-guided Ensemble for Scribble Supervised Medical Image Segmentation
Creating fully annotated labels for medical image segmentation is prohibitively time-intensive and costly, emphasizing the necessity for innovative approaches that minimize reliance on detailed annotations. Scribble anno…
Image SegmentationMedical Image SegmentationPseudo LabelSegmentation+2Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation
The features of self-supervised vision transformers (ViTs) contain strong semantic and positional information relevant to downstream tasks like object localization and segmentation. Recent works combine these features wi…
Clusteringgraph partitioningObject LocalizationProperty Prediction+2From Few to More: Scribble-based Medical Image Segmentation via Masked Context Modeling and Continuous Pseudo Labels
Scribble-based weakly supervised segmentation techniques offer comparable performance to fully supervised methods while significantly reducing annotation costs, making them an appealing alternative. Existing methods ofte…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+1Scribbles for All: Benchmarking Scribble Supervised Segmentation Across Datasets
In this work, we introduce Scribbles for All, a label and training data generation algorithm for semantic segmentation trained on scribble labels. Training or fine-tuning semantic segmentation models with weak supervisio…
AllBenchmarkingSegmentationSemantic Segmentation+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 segmentationModel Guidance via Explanations Turns Image Classifiers into Segmentation Models
Heatmaps generated on inputs of image classification networks via explainable AI methods like Grad-CAM and LRP have been observed to resemble segmentations of input images in many cases. Consequently, heatmaps have also …
Decoderimage-classificationImage ClassificationImage Segmentation+3Competing for pixels: a self-play algorithm for weakly-supervised segmentation
Weakly-supervised segmentation (WSS) methods, reliant on image-level labels indicating object presence, lack explicit correspondence between labels and regions of interest (ROIs), posing a significant challenge. Despite …
Binary ClassificationImage SegmentationObjectReinforcement Learning (RL)+3DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
Weakly supervised segmentation methods have gained significant attention due to their ability to reduce the reliance on costly pixel-level annotations during model training. However, the current weakly supervised nuclei …
Domain AdaptationPseudo LabelSegmentationWeakly supervised segmentationToNNO: Tomographic Reconstruction of a Neural Network's Output for Weakly Supervised Segmentation of 3D Medical Images
Annotating lots of 3D medical images for training segmentation models is time-consuming. The goal of weakly supervised semantic segmentation is to train segmentation models without using any ground truth segmentation mas…
Image SegmentationMedical Image SegmentationSegmentationSemantic Segmentation+3FreeSeg-Diff: Training-Free Open-Vocabulary Segmentation with Diffusion Models
Foundation models have exhibited unprecedented capabilities in tackling many domains and tasks. Models such as CLIP are currently widely used to bridge cross-modal representations, and text-to-image diffusion models are …
Image GenerationImage SegmentationSegmentationSemantic Segmentation+1WeakSurg: Weakly supervised surgical instrument segmentation using temporal equivariance and semantic continuity
For robotic surgical videos, instrument presence annotations are typically recorded with video streams, which offering the potential to reduce the manually annotated costs for segmentation. However, weakly supervised sur…
Instance SegmentationInstrument RecognitionRepresentation LearningSegmentation+2Floor Plan Image Segmentation Via Scribble-Based Semi-Weakly Supervised Learning: A Style and Category-Agnostic Approach
The field of architectural design is experiencing a transformative shift towards the integration of advanced computational methodologies, aiming to revolutionize traditional practices through automation. A pivotal aspect…
Image SegmentationSegmentationSemantic SegmentationSemi-Supervised Semantic Segmentation+2Weakly Supervised Segmentation of Vertebral Bodies with Iterative Slice-propagation
Vertebral body (VB) segmentation is an important preliminary step towards medical visual diagnosis for spinal diseases. However, most previous works require pixel/voxel-wise strong supervisions, which is expensive, tedio…
SegmentationWeakly supervised segmentation