Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification

• URL: http://arxiv.org/abs/2109.09818v7
• Date: Thu, 27 Apr 2023 08:38:48 GMT
• Title: Skin Deep Unlearning: Artefact and Instrument Debiasing in the Context of Melanoma Classification
• Authors: Peter J. Bevan and Amir Atapour-Abarghouei
• Abstract summary: Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images. But prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment. In this work, we robustly remove bias and spurious variation from an automated melanoma classification pipeline using two leading bias unlearning techniques.
• Score: 5.71097144710995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks have demonstrated dermatologist-level performance in the classification of melanoma from skin lesion images, but prediction irregularities due to biases seen within the training data are an issue that should be addressed before widespread deployment is possible. In this work, we robustly remove bias and spurious variation from an automated melanoma classification pipeline using two leading bias unlearning techniques. We show that the biases introduced by surgical markings and rulers presented in previous studies can be reasonably mitigated using these bias removal methods. We also demonstrate the generalisation benefits of unlearning spurious variation relating to the imaging instrument used to capture lesion images. Our experimental results provide evidence that the effects of each of the aforementioned biases are notably reduced, with different debiasing techniques excelling at different tasks.

Related papers

• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders [55.41644538483948]
  Automated classification and similarity retrieval aid physicians in decision-making to diagnose possible genetic conditions as early as possible. Previous work has addressed the problem as a classification problem and used deep learning methods. In this study, we used a facial recognition model trained on a large corpus of healthy individuals as a pre-task and transferred it to facial phenotype recognition.
  arXiv  Detail & Related papers  (2022-10-23T11:52:57Z)
• FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning [11.883809920936619]
  We propose FairDisCo, a disentanglement deep learning framework with contrastive learning. We compare FairDisCo to three fairness methods, namely, resampling, reweighting, and attribute-aware. We adapt two fairness-based metrics DPM and EOM for our multiple classes and sensitive attributes task, highlighting the skin-type bias in skin lesion classification.
  arXiv  Detail & Related papers  (2022-08-22T01:54:23Z)
• Artifact-Based Domain Generalization of Skin Lesion Models [20.792979998188848]
  We propose a pipeline that relies on artifacts annotation to enable generalization evaluation and debiasing. We create environments based on skin lesion artifacts to enable domain generalization methods. Our results raise a concern that debiasing models towards a single aspect may not be enough for fair skin lesion analysis.
  arXiv  Detail & Related papers  (2022-08-20T22:25:09Z)
• DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images [44.62475518267084]
  We debias deep learning models during training against unknown bias. We use control regions as surrogates that carry information regarding the bias. Applying the proposed method to learn from data exhibiting a strong bias, it near-perfectly recovers the classification performance observed when training with corresponding unbiased data.
  arXiv  Detail & Related papers  (2022-05-26T12:18:48Z)
• Detecting Melanoma Fairly: Skin Tone Detection and Debiasing for Skin Lesion Classification [5.71097144710995]
  We use a modified variational autoencoder to uncover skin tone bias in datasets commonly used as benchmarks. We propose an efficient yet effective algorithm for automatically labelling the skin tone of lesion images. We subsequently use two leading bias unlearning techniques to mitigate skin tone bias.
  arXiv  Detail & Related papers  (2022-02-06T18:53:06Z)
• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation [77.71161225100927]
  Anomaly detection is a fundamental yet challenging problem in machine learning. We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-11-25T03:53:43Z)
• On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy [70.71457102672545]
  We compare the impact of different training procedures for diabetic retinopathy grading. We investigate different aspects such as quantitative performance, statistics of the learned feature representations, interpretability and robustness to image distortions. Our results indicate that models from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.
  arXiv  Detail & Related papers  (2021-06-25T08:32:45Z)
• Learning from Failure: Training Debiased Classifier from Biased Classifier [76.52804102765931]
  We show that neural networks learn to rely on spurious correlation only when it is "easier" to learn than the desired knowledge. We propose a failure-based debiasing scheme by training a pair of neural networks simultaneously. Our method significantly improves the training of the network against various types of biases in both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2020-07-06T07:20:29Z)
• Melanoma Detection using Adversarial Training and Deep Transfer Learning [6.22964000148682]
  We propose a two-stage framework for automatic classification of skin lesion images. In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis. In the second stage, we train a deep convolutional neural network for skin lesion classification.
  arXiv  Detail & Related papers  (2020-04-14T22:46:20Z)

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.