585 research outputs found
Aplicación de Machine Learning sobre imágenes utilizadas en proyectivas
Esta línea de investigación se centra en el estudio y aplicación de técnicas de aprendizaje automático, específicamente, redes neuronales convolucionales y aprendizaje profundo para la resolución de problemas sobre reconocimiento de patrones en imágenes y videos. En esta investigación se pretende analizar las imágenes que se obtienen al aplicar técnicas proyectivas en el campo de la psicología, que permitan a los psicólogos acceder al conocimiento de la subjetividad.Red de Universidades con Carreras en Informátic
Towards Robust Artificial Intelligence Systems
Adoption of deep neural networks (DNNs) into safety-critical and high-assurance systems has been hindered by the inability of DNNs to handle adversarial and out-of-distribution input. State-of-the-art DNNs misclassify adversarial input and give high confidence output for out-of-distribution input. We attempt to solve this problem by employing two approaches, first, by detecting adversarial input and, second, by developing a confidence metric that can indicate when a DNN system has reached its limits and is not performing to the desired specifications. The effectiveness of our method at detecting adversarial input is demonstrated against the popular DeepFool adversarial image generation method. On a benchmark of 50,000 randomly chosen ImageNet adversarial images generated for CaffeNet and GoogLeNet DNNs, our method can recover the correct label with 95.76% and 97.43% accuracy, respectively. The proposed attribution-based confidence (ABC) metric utilizes attributions used to explain DNN output to characterize whether an output corresponding to an input to the DNN can be trusted. The attribution based approach removes the need to store training or test data or to train an ensemble of models to obtain confidence scores. Hence, the ABC metric can be used when only the trained DNN is available during inference. We test the effectiveness of the ABC metric against both adversarial and out-of-distribution input. We experimental demonstrate that the ABC metric is high for ImageNet input and low for adversarial input generated by FGSM, PGD, DeepFool, CW, and adversarial patch methods. For a DNN trained on MNIST images, ABC metric is high for in-distribution MNIST input and low for out-of-distribution Fashion-MNIST and notMNIST input
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Artificial intelligence (AI) models are increasingly finding applications in
the field of medicine. Concerns have been raised about the explainability of
the decisions that are made by these AI models. In this article, we give a
systematic analysis of explainable artificial intelligence (XAI), with a
primary focus on models that are currently being used in the field of
healthcare. The literature search is conducted following the preferred
reporting items for systematic reviews and meta-analyses (PRISMA) standards for
relevant work published from 1 January 2012 to 02 February 2022. The review
analyzes the prevailing trends in XAI and lays out the major directions in
which research is headed. We investigate the why, how, and when of the uses of
these XAI models and their implications. We present a comprehensive examination
of XAI methodologies as well as an explanation of how a trustworthy AI can be
derived from describing AI models for healthcare fields. The discussion of this
work will contribute to the formalization of the XAI field.Comment: 15 pages, 3 figures, accepted for publication in the IEEE
Transactions on Artificial Intelligenc
Deep Interpretability Methods for Neuroimaging
Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Nevertheless, the difficulty of reliable training on high-dimensional but small-sample datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this dissertation, we address these challenges by proposing a deep learning framework to learn from high-dimensional dynamical data while maintaining stable, ecologically valid interpretations. The developed model is pre-trainable and alleviates the need to collect an enormous amount of neuroimaging samples to achieve optimal training.
We also provide a quantitative validation module, Retain and Retrain (RAR), that can objectively verify the higher predictability of the dynamics learned by the model. Results successfully demonstrate that the proposed framework enables learning the fMRI dynamics directly from small data and capturing compact, stable interpretations of features predictive of function and dysfunction. We also comprehensively reviewed deep interpretability literature in the neuroimaging domain. Our analysis reveals the ongoing trend of interpretability practices in neuroimaging studies and identifies the gaps that should be addressed for effective human-machine collaboration in this domain.
This dissertation also proposed a post hoc interpretability method, Geometrically Guided Integrated Gradients (GGIG), that leverages geometric properties of the functional space as learned by a deep learning model. With extensive experiments and quantitative validation on MNIST and ImageNet datasets, we demonstrate that GGIG outperforms integrated gradients (IG), which is considered to be a popular interpretability method in the literature. As GGIG is able to identify the contours of the discriminative regions in the input space, GGIG may be useful in various medical imaging tasks where fine-grained localization as an explanation is beneficial
Interpretability and Explainability: A Machine Learning Zoo Mini-tour
In this review, we examine the problem of designing interpretable and
explainable machine learning models. Interpretability and explainability lie at
the core of many machine learning and statistical applications in medicine,
economics, law, and natural sciences. Although interpretability and
explainability have escaped a clear universal definition, many techniques
motivated by these properties have been developed over the recent 30 years with
the focus currently shifting towards deep learning methods. In this review, we
emphasise the divide between interpretability and explainability and illustrate
these two different research directions with concrete examples of the
state-of-the-art. The review is intended for a general machine learning
audience with interest in exploring the problems of interpretation and
explanation beyond logistic regression or random forest variable importance.
This work is not an exhaustive literature survey, but rather a primer focusing
selectively on certain lines of research which the authors found interesting or
informative
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