Effective Training Methods for Noisy Brain MRI Classification

Kernekoncepter

Training methods ISR and ISP improve model robustness against noisy MRI data.

Resumé

Correctly classifying brain tumors is crucial for treatment. Noise affects classification accuracy. ISR and ISP reweigh or perturb training samples to improve model robustness. Comparison with baselines shows superior performance of ISR and ISP.

Statistik

Correctly classifying brain tumors is imperative to the prompt and accurate treatment of a patient.
Most assume the unrealistic setting of noise-free training data.
ISR and ISP harden the classification model against noisy training data without significantly affecting generalization ability.
Empirical results show that ISR and ISP can efficiently train deep learning models robust against noisy training data.
The dataset contains 3,064 brain T1-weighted CE-MRI slices with a size of 512 × 512 from 233 patients.
We use a Convolutional Neural Network (CNN) as our classification model.

Citater

"Influence-based Sample Reweighing (ISR) and Influence-based Sample Perturbation (ISP), which are based on influence functions from robust statistics."
"ISP leads the attention of the classifier to the critical regions of an image by adding more perturbation to those areas."
"Our comprehensive experiments showed that models trained with our methods are stable and robust against different noise distributions."

Vigtigste indsigter udtrukket fra

Robust Influence-based Training Methods for Noisy Brain MRI

by Minh-Hao Van... kl. arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10698.pdf

Dybere Forespørgsler

How can these training methods be adapted for other types of medical imaging

These training methods can be adapted for other types of medical imaging by adjusting the model architecture and data preprocessing techniques to suit the specific characteristics of different modalities. For instance, in X-ray or CT images, where noise patterns may differ from MRI images, the influence-based training methods can be tailored by considering the unique noise profiles. Additionally, incorporating domain-specific features extraction methods and data augmentation techniques relevant to each modality can enhance the robustness of the models trained on these datasets.

What are the potential limitations or drawbacks of using influence-based training methods

While influence-based training methods offer significant advantages in improving model robustness against noisy data, they also have potential limitations. One drawback is that calculating influence scores for each sample in large datasets can be computationally intensive, especially when dealing with high-dimensional medical image data. Moreover, these methods may require a well-defined validation set to estimate influences accurately, which could pose challenges if labeled validation data is limited or not representative of real-world variations. Additionally, there might be cases where certain samples have misleadingly high or low influence scores due to outliers or noisy labels.

How might these methods impact the field of medical image analysis in the future

The adoption of influence-based training methods in medical image analysis holds great promise for advancing research and clinical applications in the future. These methods could lead to more reliable and interpretable deep learning models capable of handling noisy input data commonly encountered in medical imaging tasks. By enhancing model generalization ability through reweighing or perturbing influential samples based on their impact on loss functions during training, these approaches are likely to improve diagnostic accuracy and reduce misclassifications caused by noise artifacts. Furthermore, as advancements continue in computational efficiency and algorithm optimization techniques related to influence functions estimation, we can expect broader implementation across various medical imaging modalities leading to more robust and accurate diagnostic tools benefiting healthcare providers and patients alike.