Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge

• URL: http://arxiv.org/abs/2109.00823v1
• Date: Thu, 2 Sep 2021 10:09:02 GMT
• Title: Rotation Invariance and Extensive Data Augmentation: a strategy for the Mitosis Domain Generalization (MIDOG) Challenge
• Authors: Maxime W. Lafarge and Viktor H. Koelzer
• Abstract summary: We present the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen target scanners. We propose a solution based on a combination of state-of-the-art deep learning methods.
• Score: 1.52292571922932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated detection of mitotic figures in histopathology images is a challenging task: here, we present the different steps that describe the strategy we applied to participate in the MIDOG 2021 competition. The purpose of the competition was to evaluate the generalization of solutions to images acquired with unseen target scanners (hidden for the participants) under the constraint of using training data from a limited set of four independent source scanners. Given this goal and constraints, we joined the challenge by proposing a straight-forward solution based on a combination of state-of-the-art deep learning methods with the aim of yielding robustness to possible scanner-related distributional shifts at inference time. Our solution combines methods that were previously shown to be efficient for mitosis detection: hard negative mining, extensive data augmentation, rotation-invariant convolutional networks. We trained five models with different splits of the provided dataset. The subsequent classifiers produced F1-scores with a mean and standard deviation of 0.747+/-0.032 on the test splits. The resulting ensemble constitutes our candidate algorithm: its automated evaluation on the preliminary test set of the challenge returned a F1-score of 0.6828.

Related papers

• Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning [50.809769498312434]
  We propose a novel dataset pruning method termed as Temporal Dual-Depth Scoring (TDDS) Our method achieves 54.51% accuracy with only 10% training data, surpassing random selection by 7.83% and other comparison methods by at least 12.69%.
  arXiv  Detail & Related papers  (2023-11-22T03:45:30Z)
• Image-level supervision and self-training for transformer-based cross-modality tumor segmentation [2.29206349318258]
  We propose a new semi-supervised training strategy called MoDATTS. MoDATTS is designed for accurate cross-modality 3D tumor segmentation on unpaired bi-modal datasets. We report that 99% and 100% of this maximum performance can be attained if 20% and 50% of the target data is annotated.
  arXiv  Detail & Related papers  (2023-09-17T11:50:12Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem [0.0]
  We present a deeper analysis of how data augmentation techniques improve segmentation performance on medical images. We propose a novel data augmentation technique based on Generative Adversarial Networks (GANs) to create new healthy and unhealthy lung CT images. Our findings show that GAN-based techniques and spatial-level transformations are the most promising for improving the learning of deep models on this problem.
  arXiv  Detail & Related papers  (2023-03-10T13:41:20Z)
• Fine-Grained Hard Negative Mining: Generalizing Mitosis Detection with a Fifth of the MIDOG 2022 Dataset [1.2183405753834562]
  We describe a candidate deep learning solution for the Mitosis Domain Generalization Challenge 2022 (MIDOG) Our approach consists in training a rotation-invariant deep learning model using aggressive data augmentation. Our model ensemble achieved a F1-score of.697 on the final test set after automated evaluation.
  arXiv  Detail & Related papers  (2023-01-03T13:06:44Z)
• Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation [7.327638441664658]
  We present an approach for VS and cochlea segmentation in an unsupervised domain adaptation setting. We first develop a cross-site cross-modality unpaired image translation strategy to enrich the diversity of the synthesized data. Then, we devise a rule-based offline augmentation technique to further minimize the domain gap. Lastly, we adopt a self-configuring segmentation framework empowered by self-training to obtain the final results.
  arXiv  Detail & Related papers  (2022-09-23T22:26:51Z)
• DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup [58.894901088797376]
  federated learning has emerged as a promising approach for training a global model using data from multiple organizations without leaking their raw data. We propose a general framework to solve the above two challenges simultaneously. We provide comprehensive theoretical analysis including robustness analysis, convergence analysis, and generalization ability.
  arXiv  Detail & Related papers  (2022-04-16T08:08:29Z)
• The Group Loss++: A deeper look into group loss for deep metric learning [65.19665861268574]
  Group Loss is a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group. We show state-of-the-art results on clustering and image retrieval on four datasets, and present competitive results on two person re-identification datasets.
  arXiv  Detail & Related papers  (2022-04-04T14:09:58Z)
• Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge [2.072197863131669]
  The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners. We present a short summary of the approach employed by the TIA Centre team to address this challenge.
  arXiv  Detail & Related papers  (2021-09-02T11:44:42Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)

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.