Comparing interpretation methods in mental state decoding analyses with
deep learning models
- URL: http://arxiv.org/abs/2205.15581v1
- Date: Tue, 31 May 2022 07:43:02 GMT
- Title: Comparing interpretation methods in mental state decoding analyses with
deep learning models
- Authors: Armin W. Thomas and Christopher R\'e and Russell A. Poldrack
- Abstract summary: We compare the explanations of prominent interpretation methods for the mental state decoding decisions of DL models trained on three fMRI datasets.
We find that interpretation methods that focus on how sensitively a model's decoding decision changes with the values of the input produce explanations that better match with the results of a standard general linear model analysis of the fMRI data.
- Score: 8.00426138461057
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Deep learning (DL) methods find increasing application in mental state
decoding, where researchers seek to understand the mapping between mental
states (such as accepting or rejecting a gamble) and brain activity, by
identifying those brain regions (and networks) whose activity allows to
accurately identify (i.e., decode) these states. Once DL models have been
trained to accurately decode a set of mental states, neuroimaging researchers
often make use of interpretation methods from explainable artificial
intelligence research to understand their learned mappings between mental
states and brain activity. Here, we compare the explanations of prominent
interpretation methods for the mental state decoding decisions of DL models
trained on three functional Magnetic Resonance Imaging (fMRI) datasets. We find
that interpretation methods that capture the model's decision process well, by
producing faithful explanations, generally produce explanations that are less
in line with the results of standard analyses of the fMRI data, when compared
to the explanations of interpretation methods with less explanation
faithfulness. Specifically, we find that interpretation methods that focus on
how sensitively a model's decoding decision changes with the values of the
input produce explanations that better match with the results of a standard
general linear model analysis of the fMRI data, while interpretation methods
that focus on identifying the specific contribution of an input feature's value
to the decoding decision produce overall more faithful explanations that align
less well with the results of standard analyses of the fMRI data.
Related papers
- A generative framework to bridge data-driven models and scientific theories in language neuroscience [84.76462599023802]
We present generative explanation-mediated validation, a framework for generating concise explanations of language selectivity in the brain.
We show that explanatory accuracy is closely related to the predictive power and stability of the underlying statistical models.
arXiv Detail & Related papers (2024-10-01T15:57:48Z) - MindFormer: Semantic Alignment of Multi-Subject fMRI for Brain Decoding [50.55024115943266]
We introduce a novel semantic alignment method of multi-subject fMRI signals using so-called MindFormer.
This model is specifically designed to generate fMRI-conditioned feature vectors that can be used for conditioning Stable Diffusion model for fMRI- to-image generation or large language model (LLM) for fMRI-to-text generation.
Our experimental results demonstrate that MindFormer generates semantically consistent images and text across different subjects.
arXiv Detail & Related papers (2024-05-28T00:36:25Z) - Explaining Text Similarity in Transformer Models [52.571158418102584]
Recent advances in explainable AI have made it possible to mitigate limitations by leveraging improved explanations for Transformers.
We use BiLRP, an extension developed for computing second-order explanations in bilinear similarity models, to investigate which feature interactions drive similarity in NLP models.
Our findings contribute to a deeper understanding of different semantic similarity tasks and models, highlighting how novel explainable AI methods enable in-depth analyses and corpus-level insights.
arXiv Detail & Related papers (2024-05-10T17:11:31Z) - Enhancing Deep Learning Model Explainability in Brain Tumor Datasets using Post-Heuristic Approaches [1.325953054381901]
This study addresses the inherent lack of explainability during decision-making processes.
The primary focus is directed towards refining the explanations generated by the LIME Library and LIME image explainer.
Our proposed post-heuristic approach demonstrates significant advancements, yielding more robust and concrete results.
arXiv Detail & Related papers (2024-04-30T13:59:13Z) - Robust and Interpretable Medical Image Classifiers via Concept
Bottleneck Models [49.95603725998561]
We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts.
Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
arXiv Detail & Related papers (2023-10-04T21:57:09Z) - NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical
Development Patterns of Preterm Infants [73.85768093666582]
We propose an explainable geometric deep network dubbed NeuroExplainer.
NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
arXiv Detail & Related papers (2023-01-01T12:48:12Z) - Visual Interpretable and Explainable Deep Learning Models for Brain
Tumor MRI and COVID-19 Chest X-ray Images [0.0]
We evaluate attribution methods for illuminating how deep neural networks analyze medical images.
We attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models.
arXiv Detail & Related papers (2022-08-01T16:05:14Z) - TorchEsegeta: Framework for Interpretability and Explainability of
Image-based Deep Learning Models [0.0]
Clinicians are often sceptical about applying automatic image processing approaches, especially deep learning based methods, in practice.
This paper presents approaches that help to interpret and explain the results of deep learning algorithms by depicting the anatomical areas which influence the decision of the algorithm most.
Research presents a unified framework, TorchEsegeta, for applying various interpretability and explainability techniques for deep learning models.
arXiv Detail & Related papers (2021-10-16T01:00:15Z) - Going Beyond Saliency Maps: Training Deep Models to Interpret Deep
Models [16.218680291606628]
Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders.
We propose to train simulator networks that can warp a given image to inject or remove patterns of the disease.
We apply our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of the Alzheimer's disease and alcohol use disorder.
arXiv Detail & Related papers (2021-02-16T15:57:37Z) - Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
We propose a deep co-attention network for multi-view subspace learning.
It aims to extract both the common information and the complementary information in an adversarial setting.
In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
arXiv Detail & Related papers (2021-02-15T18:46:44Z) - Explaining black-box text classifiers for disease-treatment information
extraction [12.323983512532651]
A post-hoc explanation method can approximate the behavior of a black-box AI model.
incorporating medical concepts and semantics into the explanation process, our explanator finds semantic relations between inputs and outputs.
arXiv Detail & Related papers (2020-10-21T09:58:00Z)
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.