ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation

• URL: http://arxiv.org/abs/2312.05407v2
• Date: Tue, 15 Oct 2024 13:27:42 GMT
• Title: ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation
• Authors: Md Shazid Islam, Sayak Nag, Arindam Dutta, Miraj Ahmed, Fahim Faisal Niloy, Amit K. Roy-Chowdhury,
• Abstract summary: Domain adaptive segmentation typically relies on self-training using pseudo labels predicted by a pre-trained network on an unlabeled target dataset. The acquisition of pixel-level annotations across all images in a batch often leads to redundant information while increasing temporal overhead in online learning. We propose a novel image-pruning strategy that selects the most useful subset of images from the current batch for active learning.
• Score: 16.90507882617707
• License:
• Abstract: Unsupervised domain adaptive segmentation typically relies on self-training using pseudo labels predicted by a pre-trained network on an unlabeled target dataset. However, the noisy nature of such pseudo-labels presents a major bottleneck in adapting a network to the distribution shift between source and target datasets. This challenge is exaggerated when the network encounters an incoming data stream in online fashion, where the network is constrained to adapt to incoming streams of target domain data in exactly one round of forward and backward passes. In this scenario, relying solely on inaccurate pseudo-labels can lead to low-quality segmentation, which is detrimental to medical image analysis where accuracy and precision are of utmost priority. We hypothesize that a small amount of pixel-level annotation obtained from an expert can address this problem, thereby enhancing the performance of domain adaptation of online streaming data, even in the absence of dedicated training data. We call our method ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation that adapts to each incoming data batch in an online setup, incorporating feedback from an expert through active learning. Through active learning, the most informative pixels in each image can be selected for expert annotation. However, the acquisition of pixel-level annotations across all images in a batch often leads to redundant information while increasing temporal overhead in online learning. To reduce the annotation acquisition time and make the adaptation process more online-friendly, we further propose a novel image-pruning strategy that selects the most useful subset of images from the current batch for active learning. Our proposed approach outperforms existing online adaptation approaches and produces competitive results compared to offline domain adaptive active learning methods.

Related papers

• Unsupervised domain adaptation by learning using privileged information [6.748420131629902]
  We show that training-time access to side information in the form of auxiliary variables can help relax restrictions on input variables. We propose a simple two-stage learning algorithm, inspired by our analysis of the expected error in the target domain, and a practical end-to-end variant for image classification.
  arXiv  Detail & Related papers  (2023-03-16T14:31:50Z)
• Self-Supervised Pretraining for 2D Medical Image Segmentation [0.0]
  Self-supervised learning offers a way to lower the need for manually annotated data by pretraining models for a specific domain on unlabelled data. We find that self-supervised pretraining on natural images and target-domain-specific images leads to the fastest and most stable downstream convergence. In low-data scenarios, supervised ImageNet pretraining achieves the best accuracy, requiring less than 100 annotated samples to realise close to minimal error.
  arXiv  Detail & Related papers  (2022-09-01T09:25:22Z)
• Domain-invariant Prototypes for Semantic Segmentation [30.932130453313537]
  We present an easy-to-train framework that learns domain-invariant prototypes for domain adaptive semantic segmentation. Our method involves only one-stage training and does not need to be trained on large-scale un-annotated target images.
  arXiv  Detail & Related papers  (2022-08-12T02:21:05Z)
• DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation [78.30720731968135]
  Unsupervised domain adaptation in semantic segmentation has been raised to alleviate the reliance on expensive pixel-wise annotations. We propose DecoupleNet that alleviates source domain overfitting and enables the final model to focus more on the segmentation task. We also put forward Self-Discrimination (SD) and introduce an auxiliary classifier to learn more discriminative target domain features with pseudo labels.
  arXiv  Detail & Related papers  (2022-07-20T15:47:34Z)
• HYLDA: End-to-end Hybrid Learning Domain Adaptation for LiDAR Semantic Segmentation [13.87939140266266]
  This paper addresses the problem of training a LiDAR semantic segmentation network using a fully-labeled source dataset and a target dataset that only has a small number of labels. We develop a novel image-to-image translation engine, and couple it with a LiDAR semantic segmentation network, resulting in an integrated domain adaptation architecture we call HYLDA.
  arXiv  Detail & Related papers  (2022-01-14T18:13:09Z)
• Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation [19.55572909866489]
  We propose a region-based active learning approach for semantic segmentation under a domain shift. Our algorithm, Active Learning via Region Impurity and Prediction Uncertainty (AL-RIPU), introduces a novel acquisition strategy characterizing the spatial adjacency of image regions. Our method only requires very few annotations to almost reach the supervised performance and substantially outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-25T06:40:58Z)
• WEDGE: Web-Image Assisted Domain Generalization for Semantic Segmentation [72.88657378658549]
  We propose a WEb-image assisted Domain GEneralization scheme, which is the first to exploit the diversity of web-crawled images for generalizable semantic segmentation. We also present a method which injects styles of the web-crawled images into training images on-the-fly during training, which enables the network to experience images of diverse styles with reliable labels for effective training.
  arXiv  Detail & Related papers  (2021-09-29T05:19:58Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Phase Consistent Ecological Domain Adaptation [76.75730500201536]
  We focus on the task of semantic segmentation, where annotated synthetic data are aplenty, but annotating real data is laborious. The first criterion, inspired by visual psychophysics, is that the map between the two image domains be phase-preserving. The second criterion aims to leverage ecological statistics, or regularities in the scene which are manifest in any image of it, regardless of the characteristics of the illuminant or the imaging sensor.
  arXiv  Detail & Related papers  (2020-04-10T06:58:03Z)
• Supervised Domain Adaptation using Graph Embedding [86.3361797111839]
  Domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. We propose a generic framework based on graph embedding. We show that the proposed approach leads to a powerful Domain Adaptation framework.
  arXiv  Detail & Related papers  (2020-03-09T12:25:13Z)
• Understanding Self-Training for Gradual Domain Adaptation [107.37869221297687]
  We consider gradual domain adaptation, where the goal is to adapt an initial classifier trained on a source domain given only unlabeled data that shifts gradually in distribution towards a target domain. We prove the first non-vacuous upper bound on the error of self-training with gradual shifts, under settings where directly adapting to the target domain can result in unbounded error. The theoretical analysis leads to algorithmic insights, highlighting that regularization and label sharpening are essential even when we have infinite data, and suggesting that self-training works particularly well for shifts with small Wasserstein-infinity distance.
  arXiv  Detail & Related papers  (2020-02-26T08:59:40Z)

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.