Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets

• URL: http://arxiv.org/abs/2103.04526v1
• Date: Mon, 8 Mar 2021 03:15:59 GMT
• Title: Incremental Learning for Multi-organ Segmentation with Partially Labeled Datasets
• Authors: Pengbo Liu, Li Xiao, S. Kevin Zhou
• Abstract summary: We learn a multi-organ segmentation model through incremental learning (IL) In each IL stage, we lose access to the previous annotations, whose knowledge is assumingly captured by the current model. We learn to update the organ segmentation model to include the new organs.
• Score: 8.370590211748087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There exists a large number of datasets for organ segmentation, which are partially annotated, and sequentially constructed. A typical dataset is constructed at a certain time by curating medical images and annotating the organs of interest. In other words, new datasets with annotations of new organ categories are built over time. To unleash the potential behind these partially labeled, sequentially-constructed datasets, we propose to learn a multi-organ segmentation model through incremental learning (IL). In each IL stage, we lose access to the previous annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs. We give the first attempt to conjecture that the different distribution is the key reason for 'catastrophic forgetting' that commonly exists in IL methods, and verify that IL has the natural adaptability to medical image scenarios. Extensive experiments on five open-sourced datasets are conducted to prove the effectiveness of our method and the conjecture mentioned above.

Related papers

• Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentation [9.240202592825735]
  This paper proposes a two-stage multi-organ segmentation method based on mutual learning. In the first stage, each partial-organ segmentation model utilizes the non-overlapping organ labels from different datasets. In the second stage, each full-organ segmentation model is supervised by fully labeled datasets with pseudo labels.
  arXiv  Detail & Related papers  (2024-07-17T14:41:25Z)
• Universal and Extensible Language-Vision Models for Organ Segmentation and Tumor Detection from Abdominal Computed Tomography [50.08496922659307]
  We propose a universal framework enabling a single model, termed Universal Model, to deal with multiple public datasets and adapt to new classes. Firstly, we introduce a novel language-driven parameter generator that leverages language embeddings from large language models. Secondly, the conventional output layers are replaced with lightweight, class-specific heads, allowing Universal Model to simultaneously segment 25 organs and six types of tumors.
  arXiv  Detail & Related papers  (2024-05-28T16:55:15Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• Continual Learning for Abdominal Multi-Organ and Tumor Segmentation [15.983529525062938]
  We propose an innovative architecture designed specifically for continuous organ and tumor segmentation. Our proposed design involves replacing the conventional output layer with a suite of lightweight, class-specific heads. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones.
  arXiv  Detail & Related papers  (2023-06-01T17:59:57Z)
• Tailored Multi-Organ Segmentation with Model Adaptation and Ensemble [22.82094545786408]
  Multi-organ segmentation is a fundamental task in medical image analysis. Due to expensive labor costs and expertise, the availability of multi-organ annotations is usually limited. We propose a novel dual-stage method that consists of a Model Adaptation stage and a Model Ensemble stage.
  arXiv  Detail & Related papers  (2023-04-14T13:39:39Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• Learning Incrementally to Segment Multiple Organs in a CT Image [11.082692639365982]
  We propose to incrementally learn a multi-organ segmentation model. In each incremental learning stage, we lose the access to previous data and annotations. We experimentally discover that such a weakness mostly disappears for CT multi-organ segmentation.
  arXiv  Detail & Related papers  (2022-03-04T02:32:04Z)
• Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation [55.1248480381153]
  We propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. We show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task.
  arXiv  Detail & Related papers  (2021-03-30T13:41:12Z)
• Few-Shot Named Entity Recognition: A Comprehensive Study [92.40991050806544]
  We investigate three schemes to improve the model generalization ability for few-shot settings. We perform empirical comparisons on 10 public NER datasets with various proportions of labeled data. We create new state-of-the-art results on both few-shot and training-free settings.
  arXiv  Detail & Related papers  (2020-12-29T23:43:16Z)
• Towards Robust Partially Supervised Multi-Structure Medical Image Segmentation on Small-Scale Data [123.03252888189546]
  We propose Vicinal Labels Under Uncertainty (VLUU) to bridge the methodological gaps in partially supervised learning (PSL) under data scarcity. Motivated by multi-task learning and vicinal risk minimization, VLUU transforms the partially supervised problem into a fully supervised problem by generating vicinal labels. Our research suggests a new research direction in label-efficient deep learning with partial supervision.
  arXiv  Detail & Related papers  (2020-11-28T16:31:00Z)
• Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets [45.14004510709325]
  We propose to co-train weight-averaged models for learning a unified multi-organ segmentation network from few-organ datasets. To alleviate the noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels.
  arXiv  Detail & Related papers  (2020-08-17T08:39:16Z)

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.