Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization (MIDOG) Challenge

• URL: http://arxiv.org/abs/2108.11269v1
• Date: Wed, 25 Aug 2021 14:49:11 GMT
• Title: Domain Adversarial RetinaNet as a Reference Algorithm for the MItosis DOmain Generalization (MIDOG) Challenge
• Authors: Frauke Wilm, Katharina Breininger, Marc Aubreville
• Abstract summary: The MItosis DOmain Generalization challenge focuses on this specific domain shift for the task of mitotic figure detection. This work presents a mitotic figure detection algorithm developed as a baseline for the challenge, based on domain adversarial training.
• Score: 1.4732811715354452
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Assessing the Mitotic Count has a known high degree of intra- and inter-rater variability. Computer-aided systems have proven to decrease this variability and reduce labelling time. These systems, however, are generally highly dependent on their training domain and show poor applicability to unseen domains. In histopathology, these domain shifts can result from various sources, including different slide scanning systems used to digitize histologic samples. The MItosis DOmain Generalization challenge focuses on this specific domain shift for the task of mitotic figure detection. This work presents a mitotic figure detection algorithm developed as a baseline for the challenge, based on domain adversarial training. On the preliminary test set, the algorithm scores an F$_1$ score of 0.7514.

Related papers

• Domain generalization across tumor types, laboratories, and species -- insights from the 2022 edition of the Mitosis Domain Generalization Challenge [15.965814632791504]
  Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient outcome assessment. This work represents an overview of the challenge tasks, the strategies employed by the participants, and potential factors contributing to their success.
  arXiv  Detail & Related papers  (2023-09-27T11:44:58Z)
• Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation [72.70876977882882]
  Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. We propose a novel method for Few-Shot Unsupervised Domain Adaptation (FSUDA), where only a limited number of unlabeled target domain samples are available for training.
  arXiv  Detail & Related papers  (2023-09-03T16:02:01Z)
• Context-aware Domain Adaptation for Time Series Anomaly Detection [69.3488037353497]
  Time series anomaly detection is a challenging task with a wide range of real-world applications. Recent efforts have been devoted to time series domain adaptation to leverage knowledge from similar domains. We propose a framework that combines context sampling and anomaly detection into a joint learning procedure.
  arXiv  Detail & Related papers  (2023-04-15T02:28:58Z)
• Robust Domain Adaptive Object Detection with Unified Multi-Granularity Alignment [59.831917206058435]
  Domain adaptive detection aims to improve the generalization of detectors on target domain. Recent approaches achieve domain adaption through feature alignment in different granularities via adversarial learning. We introduce a unified multi-granularity alignment (MGA)-based detection framework for domain-invariant feature learning.
  arXiv  Detail & Related papers  (2023-01-01T08:38:07Z)
• Mitosis domain generalization in histopathology images -- The MIDOG challenge [12.69088811541426]
  Recognition of mitotic figures by pathologists is subject to a strong inter-rater bias, which limits the prognostic value. State-of-the-art deep learning methods can support the expert in this assessment but are known to strongly deteriorate when applied in a different clinical environment than was used for training. The MICCAI MIDOG 2021 challenge has been to propose and evaluate methods that derive scanner-agnostic mitosis detection algorithms.
  arXiv  Detail & Related papers  (2022-04-06T11:43:10Z)
• Robust Multi-Domain Mitosis Detection [0.0]
  We learn a target representative feature space through unpaired image to image translation (CycleGAN) This work presents a simple yet effective multi-step mitotic figure detection algorithm developed as a baseline for the MIDOG challenge.
  arXiv  Detail & Related papers  (2021-09-13T06:25:15Z)
• MitoDet: Simple and robust mitosis detection [0.31498833540989407]
  An important source of a domain shift is introduced by different microscopes and their camera systems, which noticeably change the color representation of digitized images. We present our submitted algorithm for the Mitosis Domain Generalization Challenge, which employs a RetinaNet trained with strong data augmentation and achieves an F1 score of 0.7138 on the preliminary test set.
  arXiv  Detail & Related papers  (2021-09-02T17:19:08Z)
• Unsupervised Domain Adaptation for Retinal Vessel Segmentation with Adversarial Learning and Transfer Normalization [22.186070895966022]
  We propose an entropy-based adversarial learning strategy to reduce the distribution discrepancy between source and target domains. A new transfer normalization layer is proposed to further boost the transferability of the deep network. Our approach yields significant performance gains compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-04T02:45:37Z)
• Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction [61.58601145792065]
  We learn invariant target-domain features by defining a novel self-supervised task based on retinal vessel image reconstructions. It can be shown that our approach outperforms existing domain strategies.
  arXiv  Detail & Related papers  (2021-07-20T09:44:07Z)
• A Bit More Bayesian: Domain-Invariant Learning with Uncertainty [111.22588110362705]
  Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with a probabilistic framework based on variational Bayesian inference. We derive domain-invariant representations and classifiers, which are jointly established in a two-layer Bayesian neural network.
  arXiv  Detail & Related papers  (2021-05-09T21:33:27Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)

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.