Intrinsic Bias Identification on Medical Image Datasets
- URL: http://arxiv.org/abs/2203.12872v1
- Date: Thu, 24 Mar 2022 06:28:07 GMT
- Title: Intrinsic Bias Identification on Medical Image Datasets
- Authors: Shijie Zhang and Lanjun Wang and Lian Ding and Senhua Zhu and Dandan
Tu
- Abstract summary: We first define the data intrinsic bias attribute, and then propose a novel bias identification framework for medical image datasets.
The framework contains two major components, KlotskiNet and Bias Discriminant Direction Analysis(bdda), where KlostkiNet is to build the mapping which makes backgrounds to distinguish positive and negative samples.
Experimental results on three datasets show the effectiveness of the bias attributes discovered by the framework.
- Score: 9.054785751150547
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Machine learning based medical image analysis highly depends on datasets.
Biases in the dataset can be learned by the model and degrade the
generalizability of the applications. There are studies on debiased models.
However, scientists and practitioners are difficult to identify implicit biases
in the datasets, which causes lack of reliable unbias test datasets to valid
models. To tackle this issue, we first define the data intrinsic bias
attribute, and then propose a novel bias identification framework for medical
image datasets. The framework contains two major components, KlotskiNet and
Bias Discriminant Direction Analysis(bdda), where KlostkiNet is to build the
mapping which makes backgrounds to distinguish positive and negative samples
and bdda provides a theoretical solution on determining bias attributes.
Experimental results on three datasets show the effectiveness of the bias
attributes discovered by the framework.
Related papers
- Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases [62.806300074459116]
Definition bias is a negative phenomenon that can mislead models.
We identify two types of definition bias in IE: bias among information extraction datasets and bias between information extraction datasets and instruction tuning datasets.
We propose a multi-stage framework consisting of definition bias measurement, bias-aware fine-tuning, and task-specific bias mitigation.
arXiv Detail & Related papers (2024-03-25T03:19:20Z) - Medical Image Debiasing by Learning Adaptive Agreement from a Biased
Council [8.530912655468645]
Deep learning could be prone to learning shortcuts raised by dataset bias.
Despite its significance, there is a dearth of research in the medical image classification domain to address dataset bias.
This paper proposes learning Adaptive Agreement from a Biased Council (Ada-ABC), a debiasing framework that does not rely on explicit bias labels.
arXiv Detail & Related papers (2024-01-22T06:29:52Z) - Causality and Independence Enhancement for Biased Node Classification [56.38828085943763]
We propose a novel Causality and Independence Enhancement (CIE) framework, applicable to various graph neural networks (GNNs)
Our approach estimates causal and spurious features at the node representation level and mitigates the influence of spurious correlations.
Our approach CIE not only significantly enhances the performance of GNNs but outperforms state-of-the-art debiased node classification methods.
arXiv Detail & Related papers (2023-10-14T13:56:24Z) - Feature-Level Debiased Natural Language Understanding [86.8751772146264]
Existing natural language understanding (NLU) models often rely on dataset biases to achieve high performance on specific datasets.
We propose debiasing contrastive learning (DCT) to mitigate biased latent features and neglect the dynamic nature of bias.
DCT outperforms state-of-the-art baselines on out-of-distribution datasets while maintaining in-distribution performance.
arXiv Detail & Related papers (2022-12-11T06:16:14Z) - Pseudo Bias-Balanced Learning for Debiased Chest X-ray Classification [57.53567756716656]
We study the problem of developing debiased chest X-ray diagnosis models without knowing exactly the bias labels.
We propose a novel algorithm, pseudo bias-balanced learning, which first captures and predicts per-sample bias labels.
Our proposed method achieved consistent improvements over other state-of-the-art approaches.
arXiv Detail & Related papers (2022-03-18T11:02:18Z) - General Greedy De-bias Learning [163.65789778416172]
We propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model like gradient descent in functional space.
GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
arXiv Detail & Related papers (2021-12-20T14:47:32Z) - Certifying Robustness to Programmable Data Bias in Decision Trees [12.060443368097102]
We certify that models produced by a learning learner are pointwise-robust to potential dataset biases.
Our approach allows specifying bias models across a variety of dimensions.
We evaluate our approach on datasets commonly used in the fairness literature.
arXiv Detail & Related papers (2021-10-08T20:15:17Z) - Learning Debiased Representation via Disentangled Feature Augmentation [19.348340314001756]
This paper presents an empirical analysis revealing that training with "diverse" bias-conflicting samples is crucial for debiasing.
We propose a novel feature-level data augmentation technique in order to synthesize diverse bias-conflicting samples.
arXiv Detail & Related papers (2021-07-03T08:03:25Z) - Towards Robustifying NLI Models Against Lexical Dataset Biases [94.79704960296108]
This paper explores both data-level and model-level debiasing methods to robustify models against lexical dataset biases.
First, we debias the dataset through data augmentation and enhancement, but show that the model bias cannot be fully removed via this method.
The second approach employs a bag-of-words sub-model to capture the features that are likely to exploit the bias and prevents the original model from learning these biased features.
arXiv Detail & Related papers (2020-05-10T17:56:10Z)
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.