XAI approach for addressing the dataset shift problem: BCI as a case study

In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions leading ML systems toward poor generalisation performances. Therefore, such systems can be unreliable and risky, particularly when used in safety-critical domains. This problem is intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are used. In fact, EEG signals are highly non-stationary signals both over time and between different subjects. Despite several efforts in developing BCI systems to deal with different acquisition times or subjects, performance in many BCI applications remains low. Exploiting the knowledge from eXplainable Artificial Intelligence (XAI) methods can help develop EEG-based AI approaches, overcoming the performance returned by the current ones. The proposed framework will give greater robustness and reliability to BCI systems with respect to the current state of the art, alleviating the dataset shift problem and allowing a BCI system to be used by different subjects at different times without the need for further calibration/training stages

Toward the application of XAI methods in EEG-based systems

An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself

Digital image analysis of collagen assessment of progression of fibrosis in recurrent HCV after liver transplantation

Background & Aims: Histological assessment of fibrosis progression is currently performed by staging systems which are not continuous quantitative measurements. We aimed at assessing a quantitative measurement of fibrosis collagen proportionate area (CPA), to evaluate fibrosis progression and compare it to Ishak stage progression. Methods: We studied a consecutive cohort of 155 patients with recurrent HCV hepatitis after liver transplantation (LT), who had liver biopsies at one year and were subsequently evaluated for progression of fibrosis using CPA and Ishak staging, and correlated with clinical decompensation. The upper quartile of distribution of fibrosis rates (difference in CPA or Ishak stage between paired biopsies) defined fast fibrosers. Results: Patients had 610 biopsies and a median follow-up of 116 (18-252) months. Decompensation occurred in 29 (18%) patients. Median Ishak stage progression rate was 0.42 units/year: (24 (15%) fast fibrosers). Median CPA fibrosis progression rate was 0.71%/year (36 (23%) fast fibrosers). Clinical decompensation was independently associated by Cox regression only with CPA (p = 0.007), with AUROCs of 0.81 (95% CI 0.71-0.91) compared to 0.68 (95% CI 0.56-0.81) for Ishak stage. Fast fibrosis defined by CPA progression was independently associated with histological de novo hepatitis (OR: 3.77), older donor age (OR: 1.03) and non-use/discontinuation of azathioprine before 1 year post-LT (OR: 3.85), whereas when defined by Ishak progression, fast fibrosers was only associated with histological de novo hepatitis. Conclusions: CPA fibrosis progression rate is a better predictor of clinical outcome than progression by Ishak stage. Histological de novo hepatitis, older donor age and non-use/discontinuation of azathioprine are associated with rapid fibrosis progression in recurrent HCV chronic hepatitis after liver transplantation. © 2012 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved

SHAP-based explanations to improve classification systems

Explainable Artificial Intelligence (XAI) is a field usually dedicated to offering insights into the decision-making mechanisms of AI models. Its purpose is to enable users to comprehend the reasoning behind the results provided by these models, going beyond mere outputs. In addition, one of the main goals of XAI is to improve the performance of AI models by exploiting the explanations of their decision-making processes. However, a predominant portion of XAI research concentrates on elucidating the functioning of AI systems, with comparatively fewer studies delving into how XAI techniques can be leveraged to enhance the performance of an AI system. This underlines a potential area for further exploration and development in the field of XAI. In this paper we focus on the possibility to enhance the performance of an already trained AI model. To this aim we propose a new scheme of interaction between explanations provided by SHAP XAI method and computations of the responses of a given AI model. This new proposal was tested using the well-known CIFAR-10 dataset and EfficientNet-B2 model, showing promising results

An XAI-based masking approach to improve classification systems

Explainable Artificial Intelligence (XAI) seeks to elucidate the decision-making mechanisms of AI models, enabling users to glean insights beyond the results they produce. While a key objective of XAI is to enhance the performance of AI models through explanatory processes, a notable portion of XAI literature predominantly addresses the explanation of AI systems, with limited focus on leveraging XAI methods for performance improvement. This study introduces a novel approach utilizing Integrated Gradients explanations to enhance a classification system, which is subsequently evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Empirical findings indicate that Integrated Gradients explanations effectively contribute to enhancing classification performance

The cannabinoid WIN 55,212-2 prevents neuroendocrine differentiation of LNCaP prostate cancer cells

BACKGROUND: Neuroendocrine (NE) differentiation represents a common feature of prostate cancer and is associated with accelerated disease progression and poor clinical outcome. Nowadays, there is no treatment for this aggressive form of prostate cancer. The aim of this study was to determine the influence of the cannabinoid WIN 55,212-2 (WIN, a non-selective cannabinoid CB1 and CB2 receptor agonist) on the NE differentiation of prostate cancer cells.METHODS: NE differentiation of prostate cancer LNCaP cells was induced by serum deprivation or by incubation with interleukin-6, for 6 days. Levels of NE markers and signaling proteins were determined by western blotting. Levels of cannabinoid receptors were determined by quantitative PCR. The involvement of signaling cascades was investigated by pharmacological inhibition and small interfering RNA.RESULTS: The differentiated LNCaP cells exhibited neurite outgrowth, and increased the expression of the typical NE markers neuron-specific enolase and βIII tubulin (βIII Tub). Treatment with 3 μM WIN inhibited NK differentiation of LNCaP cells. The cannabinoid WIN downregulated the PI3K/Akt/mTOR signaling pathway, resulting in NE differentiation inhibition. In addition, an activation of AMP-activated protein kinase (AMPK) was observed in WIN-treated cells, which correlated with a decrease in the NE markers expression. Our results also show that during NE differentiation the expression of cannabinoid receptors CB1 and CB2 dramatically decreases.CONCLUSIONS: Taken together, we demonstrate that PI3K/Akt/AMPK might be an important axis modulating NE differentiation of prostate cancer that is blocked by the cannabinoid WIN, pointing to a therapeutic potential of cannabinoids against NE prostate cancer.Prostate Cancer and Prostatic Diseases advance online publication, 21 June 2016; doi:10.1038/pcan.2016.19.</p