Weakly Supervised Nuclei Segmentation via Instance Learning

• URL: http://arxiv.org/abs/2202.01564v1
• Date: Thu, 3 Feb 2022 12:51:30 GMT
• Title: Weakly Supervised Nuclei Segmentation via Instance Learning
• Authors: Weizhen Liu, Qian He, Xuming He
• Abstract summary: Weakly supervised nuclei segmentation is a critical problem for pathological image analysis. We propose to decouple weakly supervised semantic and instance segmentation. Our approach achieves the state-of-the-art performance on two public benchmarks of pathological images.
• Score: 30.392562834466613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised nuclei segmentation is a critical problem for pathological image analysis and greatly benefits the community due to the significant reduction of labeling cost. Adopting point annotations, previous methods mostly rely on less expressive representations for nuclei instances and thus have difficulty in handling crowded nuclei. In this paper, we propose to decouple weakly supervised semantic and instance segmentation in order to enable more effective subtask learning and to promote instance-aware representation learning. To achieve this, we design a modular deep network with two branches: a semantic proposal network and an instance encoding network, which are trained in a two-stage manner with an instance-sensitive loss. Empirical results show that our approach achieves the state-of-the-art performance on two public benchmarks of pathological images from different types of organs.

Related papers

• Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation [50.407071700154674]
  We propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL) Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible. Extensive experiments on a couple of publicly accessible datasets demonstrate that SGFSIS can outperform other annotation-efficient learning baselines.
  arXiv  Detail & Related papers  (2024-02-26T03:49:18Z)
• Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation [19.526504045149895]
  We propose a novel image-level weakly supervised method, called cyclic learning, to solve this problem. Cyclic learning comprises a front-end classification task and a back-end semi-supervised instance segmentation task. Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation.
  arXiv  Detail & Related papers  (2023-06-05T08:32:12Z)
• Cross-supervised Dual Classifiers for Semi-supervised Medical Image Segmentation [10.18427897663732]
  Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis. This paper proposes a cross-supervised learning framework based on dual classifiers (DC-Net) Experiments on LA and Pancreas-CT dataset illustrate that DC-Net outperforms other state-of-the-art methods for semi-supervised segmentation.
  arXiv  Detail & Related papers  (2023-05-25T16:23:39Z)
• Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning [79.83792914684985]
  We prove a new identifiability result that provides conditions under which maximally sparse base-predictors yield disentangled representations. Motivated by this theoretical result, we propose a practical approach to learn disentangled representations based on a sparsity-promoting bi-level optimization problem.
  arXiv  Detail & Related papers  (2022-11-26T21:02:09Z)
• A Survey on Label-efficient Deep Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction [115.9169213834476]
  This paper offers a comprehensive review on label-efficient segmentation methods. We first develop a taxonomy to organize these methods according to the supervision provided by different types of weak labels. Next, we summarize the existing label-efficient segmentation methods from a unified perspective.
  arXiv  Detail & Related papers  (2022-07-04T06:21:01Z)
• Semantic Representation and Dependency Learning for Multi-Label Image Recognition [76.52120002993728]
  We propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide model. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions.
  arXiv  Detail & Related papers  (2022-04-08T00:55:15Z)
• Self-Ensembling GAN for Cross-Domain Semantic Segmentation [107.27377745720243]
  This paper proposes a self-ensembling generative adversarial network (SE-GAN) exploiting cross-domain data for semantic segmentation. In SE-GAN, a teacher network and a student network constitute a self-ensembling model for generating semantic segmentation maps, which together with a discriminator, forms a GAN. Despite its simplicity, we find SE-GAN can significantly boost the performance of adversarial training and enhance the stability of the model.
  arXiv  Detail & Related papers  (2021-12-15T09:50:25Z)
• Point-supervised Segmentation of Microscopy Images and Volumes via Objectness Regularization [2.243486411968779]
  This work enables the training of semantic segmentation networks on images with only a single point for training per instance. We achieve competitive results against the state-of-the-art in point-supervised semantic segmentation on challenging datasets in digital pathology.
  arXiv  Detail & Related papers  (2021-03-09T18:40:00Z)
• A Weakly-Supervised Semantic Segmentation Approach based on the Centroid Loss: Application to Quality Control and Inspection [6.101839518775968]
  We propose and assess a new weakly-supervised semantic segmentation approach making use of a novel loss function. The performance of the approach is evaluated against datasets from two different industry-related case studies.
  arXiv  Detail & Related papers  (2020-10-26T09:08:21Z)
• Weakly Supervised Deep Nuclei Segmentation Using Partial Points Annotation in Histopathology Images [51.893494939675314]
  We propose a novel weakly supervised segmentation framework based on partial points annotation. We show that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-10T15:41:29Z)
• NINEPINS: Nuclei Instance Segmentation with Point Annotations [2.19221864553448]
  We propose an algorithm for instance segmentation that uses pseudo-label segmentations generated automatically from point annotations. With the generated segmentation masks, the proposed method trains a modified version of HoVer-Net model to achieve instance segmentation. Experimental results show that the proposed method is robust to inaccuracies in point annotations and comparison with Hover-Net trained with fully annotated instance masks shows that a degradation in segmentation performance does not always imply a degradation in higher order tasks such as tissue classification.
  arXiv  Detail & Related papers  (2020-06-24T08:28:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.