Entropy Bootstrapping for Weakly Supervised Nuclei Detection
Microscopy structure segmentation, such as detecting cells or nuclei, generally requires a human to draw a ground truth contour around each instance. Weakly supervised approaches (e.g. consisting of only single point labels) have the potential to reduce this workload significantly. Our approach uses individual point labels for an entropy estimation to approximate an underlying distribution of cell pixels. We infer full cell masks from this distribution, and use Mask-RCNN to produce an instance segmentation output. We compare this point--annotated approach with training on the full ground truth masks. We show that our method achieves a comparatively good level of performance, despite a 95% reduction in pixel labels.
Code (0)
등록된 구현이 없습니다.
Tasks
Instance SegmentationSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images
Nuclei segmentation is a fundamental task in histopathology image analysis. Typically, such segmentation tasks require significant effort to manually generate accurate pixel-wise annotations for fully supervised training…
SegmentationWeakly supervised segmentationDAWN: 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 segmentationA Light-weight Interpretable Compositional Model for Nuclei Detection and Weakly-Supervised Segmentation
The field of computational pathology has witnessed great advancements since deep neural networks have been widely applied. These networks usually require large numbers of annotated data to train vast parameters. However,…
SegmentationWeakly supervised segmentationRecognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach
In the wake of a polarizing election, social media is laden with hateful content. To address various limitations of supervised hate speech classification methods including corpus bias and huge cost of annotation, we prop…
General ClassificationHate Speech DetectionBoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels
Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of the wide existence and large variances in…
Multiple Instance LearningSegmentation