2,295 research outputs found
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors
Convolutional neural network (CNN) models for computer vision are powerful
but lack explainability in their most basic form. This deficiency remains a key
challenge when applying CNNs in important domains. Recent work for explanations
through feature importance of approximate linear models has moved from
input-level features (pixels or segments) to features from mid-layer feature
maps in the form of concept activation vectors (CAVs). CAVs contain
concept-level information and could be learnt via clustering. In this work, we
rethink the ACE algorithm of Ghorbani et al., proposing an alternative
inevitable concept-based explanation (ICE) framework to overcome its
shortcomings. Based on the requirements of fidelity (approximate models to
target models) and interpretability (being meaningful to people), we design
measurements and evaluate a range of matrix factorization methods with our
framework. We find that \emph{non-negative concept activation vectors} (NCAVs)
from non-negative matrix factorization provide superior performance in
interpretability and fidelity based on computational and human subject
experiments. Our framework provides both local and global concept-level
explanations for pre-trained CNN models
Multimodal Machine Learning for Automated ICD Coding
This study presents a multimodal machine learning model to predict ICD-10
diagnostic codes. We developed separate machine learning models that can handle
data from different modalities, including unstructured text, semi-structured
text and structured tabular data. We further employed an ensemble method to
integrate all modality-specific models to generate ICD-10 codes. Key evidence
was also extracted to make our prediction more convincing and explainable. We
used the Medical Information Mart for Intensive Care III (MIMIC -III) dataset
to validate our approach. For ICD code prediction, our best-performing model
(micro-F1 = 0.7633, micro-AUC = 0.9541) significantly outperforms other
baseline models including TF-IDF (micro-F1 = 0.6721, micro-AUC = 0.7879) and
Text-CNN model (micro-F1 = 0.6569, micro-AUC = 0.9235). For interpretability,
our approach achieves a Jaccard Similarity Coefficient (JSC) of 0.1806 on text
data and 0.3105 on tabular data, where well-trained physicians achieve 0.2780
and 0.5002 respectively.Comment: Machine Learning for Healthcare 201
Methods for Interpreting and Understanding Deep Neural Networks
This paper provides an entry point to the problem of interpreting a deep
neural network model and explaining its predictions. It is based on a tutorial
given at ICASSP 2017. It introduces some recently proposed techniques of
interpretation, along with theory, tricks and recommendations, to make most
efficient use of these techniques on real data. It also discusses a number of
practical applications.Comment: 14 pages, 10 figure
An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts
In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less intelligible and tractable to humans as the model complexity increases. In this paper, we target a similarity learning task in the context of image retrieval, with a focus on the model interpretability issue. An effective similarity neural network (SNN) is proposed not only to seek robust retrieval performance but also to achieve satisfactory post-hoc interpretability. The network is designed by linking the neuron architecture with the organization of a concept tree and by formulating neuron operations to pass similarity information between concepts. Various ways of understanding and visualizing what is learned by the SNN neurons are proposed. We also exhaustively evaluate the proposed approach using a number of relevant datasets against a number of state-of-the-art approaches to demonstrate the effectiveness of the proposed network. Our results show that the proposed approach can offer superior performance when compared against state-of-the-art approaches. Neuron visualization results are demonstrated to support the understanding of the trained neurons
Explainable deep learning models in medical image analysis
Deep learning methods have been very effective for a variety of medical
diagnostic tasks and has even beaten human experts on some of those. However,
the black-box nature of the algorithms has restricted clinical use. Recent
explainability studies aim to show the features that influence the decision of
a model the most. The majority of literature reviews of this area have focused
on taxonomy, ethics, and the need for explanations. A review of the current
applications of explainable deep learning for different medical imaging tasks
is presented here. The various approaches, challenges for clinical deployment,
and the areas requiring further research are discussed here from a practical
standpoint of a deep learning researcher designing a system for the clinical
end-users.Comment: Preprint submitted to J.Imaging, MDP
Concept-based Explanations using Non-negative Concept Activation Vectors and Decision Tree for CNN Models
This paper evaluates whether training a decision tree based on concepts
extracted from a concept-based explainer can increase interpretability for
Convolutional Neural Networks (CNNs) models and boost the fidelity and
performance of the used explainer. CNNs for computer vision have shown
exceptional performance in critical industries. However, it is a significant
barrier when deploying CNNs due to their complexity and lack of
interpretability. Recent studies to explain computer vision models have shifted
from extracting low-level features (pixel-based explanations) to mid-or
high-level features (concept-based explanations). The current research
direction tends to use extracted features in developing approximation
algorithms such as linear or decision tree models to interpret an original
model. In this work, we modify one of the state-of-the-art concept-based
explanations and propose an alternative framework named TreeICE. We design a
systematic evaluation based on the requirements of fidelity (approximate models
to original model's labels), performance (approximate models to ground-truth
labels), and interpretability (meaningful of approximate models to humans). We
conduct computational evaluation (for fidelity and performance) and human
subject experiments (for interpretability) We find that Tree-ICE outperforms
the baseline in interpretability and generates more human readable explanations
in the form of a semantic tree structure. This work features how important to
have more understandable explanations when interpretability is crucial
Interpretable Medical Imagery Diagnosis with Self-Attentive Transformers: A Review of Explainable AI for Health Care
Recent advancements in artificial intelligence (AI) have facilitated its
widespread adoption in primary medical services, addressing the demand-supply
imbalance in healthcare. Vision Transformers (ViT) have emerged as
state-of-the-art computer vision models, benefiting from self-attention
modules. However, compared to traditional machine-learning approaches,
deep-learning models are complex and are often treated as a "black box" that
can cause uncertainty regarding how they operate. Explainable Artificial
Intelligence (XAI) refers to methods that explain and interpret machine
learning models' inner workings and how they come to decisions, which is
especially important in the medical domain to guide the healthcare
decision-making process. This review summarises recent ViT advancements and
interpretative approaches to understanding the decision-making process of ViT,
enabling transparency in medical diagnosis applications
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