Towards plausibility in time series counterfactual explanations

We present a new method for generating plausible counterfactual explanations for time series classification problems. The approach performs gradient-based optimization directly in the input space. To enforce plausibility, we integrate soft-DTW (dynamic time warping) alignment with $k$-nearest neighbors from the target class, which effectively encourages the generated counterfactuals to adopt a realistic temporal structure. The overall optimization objective is a multi-faceted loss function that balances key counterfactual properties. It incorporates losses for validity, sparsity, and proximity, alongside the novel soft-DTW-based plausibility component. We conduct an evaluation of our method against several strong reference approaches, measuring the key properties of the generated counterfactuals across multiple dimensions. The results demonstrate that our method achieves competitive performance in validity while significantly outperforming existing approaches in distributional alignment with the target class, indicating superior temporal realism. Furthermore, a qualitative analysis highlights the critical limitations of existing methods in preserving realistic temporal structure. This work shows that the proposed method consistently generates counterfactual explanations for time series classifiers that are not only valid but also highly plausible and consistent with temporal patterns.

Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Setting

Limited medical imaging datasets challenge deep learning models by increasing risks of overfitting and reduced generalization, particularly in Generative Adversarial Networks (GANs), where discriminators may overfit, leading to training divergence. This constraint also impairs classification models trained on small datasets. Generative Data Augmentation (GDA) addresses this by expanding training datasets with synthetic data, although it requires training a generative model. We propose and evaluate two local lesion generation approaches to address the challenge of augmenting small medical image datasets. The first approach employs the Poisson Image Editing algorithm, a classical image processing technique, to create realistic image composites that outperform current state-of-the-art methods. The second approach introduces a novel generative method, leveraging a fine-tuned Image Inpainting GAN to synthesize realistic lesions within specified regions of real training images. A comprehensive comparison of the two proposed methods demonstrates that effective local lesion generation in a data-constrained setting allows for reaching new state-of-the-art results in capsule endoscopy lesion classification. Combination of our techniques achieves a macro F1-score of 33.07%, surpassing the previous best result by 7.84 percentage points (p.p.) on the highly imbalanced Kvasir Capsule Dataset, a benchmark for capsule endoscopy. To the best of our knowledge, this work is the first to apply a fine-tuned Image Inpainting GAN for GDA in medical imaging, demonstrating that an image-conditional GAN can be adapted effectively to limited datasets to generate high-quality examples, facilitating effective data augmentation. Additionally, we show that combining this GAN-based approach with classical image processing techniques further enhances the results.