Semi-supervised framework combining self-training and cooperative-training achieves accurate signet ring cell detection on large real clinical pathology data.
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
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abstract
Semantic labelling and instance segmentation are two tasks that require particularly costly annotations. Starting from weak supervision in the form of bounding box detection annotations, we propose a new approach that does not require modification of the segmentation training procedure. We show that when carefully designing the input labels from given bounding boxes, even a single round of training is enough to improve over previously reported weakly supervised results. Overall, our weak supervision approach reaches ~95% of the quality of the fully supervised model, both for semantic labelling and instance segmentation.
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cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Signet Ring Cell Detection With a Semi-supervised Learning Framework
Semi-supervised framework combining self-training and cooperative-training achieves accurate signet ring cell detection on large real clinical pathology data.