BackMix: Mitigating Shortcut Learning in Echocardiography with Minimal Supervision

• URL: http://arxiv.org/abs/2406.19148v1
• Date: Thu, 27 Jun 2024 13:06:47 GMT
• Title: BackMix: Mitigating Shortcut Learning in Echocardiography with Minimal Supervision
• Authors: Kit Mills Bransby, Arian Beqiri, Woo-Jin Cho Kim, Jorge Oliveira, Agisilaos Chartsias, Alberto Gomez,
• Abstract summary: We propose a simple, yet effective random background augmentation method called BackMix. By enforcing the background to be uncorrelated with the outcome, the model learns to focus on the data within the ultrasound sector. We extend our method in a semi-supervised setting, finding that the positive effects of BackMix are maintained with as few as 5% of segmentation labels.
• Score: 1.3708815960776262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks can learn spurious correlations that lead to the correct prediction in a validation set, but generalise poorly because the predictions are right for the wrong reason. This undesired learning of naive shortcuts (Clever Hans effect) can happen for example in echocardiogram view classification when background cues (e.g. metadata) are biased towards a class and the model learns to focus on those background features instead of on the image content. We propose a simple, yet effective random background augmentation method called BackMix, which samples random backgrounds from other examples in the training set. By enforcing the background to be uncorrelated with the outcome, the model learns to focus on the data within the ultrasound sector and becomes invariant to the regions outside this. We extend our method in a semi-supervised setting, finding that the positive effects of BackMix are maintained with as few as 5% of segmentation labels. A loss weighting mechanism, wBackMix, is also proposed to increase the contribution of the augmented examples. We validate our method on both in-distribution and out-of-distribution datasets, demonstrating significant improvements in classification accuracy, region focus and generalisability. Our source code is available at: https://github.com/kitbransby/BackMix

Related papers

• BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors [50.09148454840245]
  Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications. Existing studies show impressive progress using unknown samples from auxiliary datasets to regularize OSR models, but they have proved to be sensitive to selecting such known outliers. We propose a new method, Background Mix (BackMix), that mixes the foreground of an image with different backgrounds to remove the underlying fore-background priors.
  arXiv  Detail & Related papers  (2025-03-22T10:23:11Z)
• SUMix: Mixup with Semantic and Uncertain Information [41.99721365685618]
  Mixup data augmentation approaches have been applied for various tasks of deep learning. We propose a novel approach named SUMix to learn the mixing ratio as well as the uncertainty for the mixed samples during the training process.
  arXiv  Detail & Related papers  (2024-07-10T16:25:26Z)
• Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation [10.208913996525055]
  We propose a Contrastive CutMix that constructs augmented samples with semantically consistent labels to boost the performance of long-tailed recognition. Our experiments show that our ConCutMix significantly improves the accuracy on tail classes as well as the overall performance.
  arXiv  Detail & Related papers  (2024-07-06T01:31:49Z)
• Selective Mixup Helps with Distribution Shifts, But Not (Only) because of Mixup [26.105340203096596]
  We show that non-random selection of pairs affects the training distribution and improve generalization by means completely unrelated to the mixing. We have found a new equivalence between two successful methods: selective mixup and resampling.
  arXiv  Detail & Related papers  (2023-05-26T10:56:22Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning [99.14132861655223]
  We propose a novel intra-class adaptive augmentation (IAA) framework for deep metric learning. We reasonably estimate intra-class variations for every class and generate adaptive synthetic samples to support hard samples mining. Our method significantly improves and outperforms the state-of-the-art methods on retrieval performances by 3%-6%.
  arXiv  Detail & Related papers  (2022-11-29T14:52:38Z)
• C-Mixup: Improving Generalization in Regression [71.10418219781575]
  Mixup algorithm improves generalization by linearly interpolating a pair of examples and their corresponding labels. We propose C-Mixup, which adjusts the sampling probability based on the similarity of the labels. C-Mixup achieves 6.56%, 4.76%, 5.82% improvements in in-distribution generalization, task generalization, and out-of-distribution robustness, respectively.
  arXiv  Detail & Related papers  (2022-10-11T20:39:38Z)
• RecursiveMix: Mixed Learning with History [21.865332756486314]
  "RecursiveMix" (RM) is a mixed-sample learning paradigm that leverages the historical input-prediction-label triplets. Based on ResNet-50, RM largely improves classification accuracy by $sim$3.2% on CIFAR100 and $sim$2.8% on ImageNet with negligible extra computation/storage costs.
  arXiv  Detail & Related papers  (2022-03-14T03:59:47Z)
• ReMix: Towards Image-to-Image Translation with Limited Data [154.71724970593036]
  We propose a data augmentation method (ReMix) to tackle this issue. We interpolate training samples at the feature level and propose a novel content loss based on the perceptual relations among samples. The proposed approach effectively reduces the ambiguity of generation and renders content-preserving results.
  arXiv  Detail & Related papers  (2021-03-31T06:24:10Z)
• Background Splitting: Finding Rare Classes in a Sea of Background [55.03789745276442]
  We focus on the real-world problem of training accurate deep models for image classification of a small number of rare categories. In these scenarios, almost all images belong to the background category in the dataset (>95% of the dataset is background) We demonstrate that both standard fine-tuning approaches and state-of-the-art approaches for training on imbalanced datasets do not produce accurate deep models in the presence of this extreme imbalance.
  arXiv  Detail & Related papers  (2020-08-28T23:05:15Z)
• DivideMix: Learning with Noisy Labels as Semi-supervised Learning [111.03364864022261]
  We propose DivideMix, a framework for learning with noisy labels. Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-02-18T06:20:06Z)

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.