Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Explainable convolutional neural networks: A taxonomy, review, and future directions
Convolutional neural networks (CNNs) have shown promising results and have
outperformed classical machine learning techniques in tasks such as image classification …
outperformed classical machine learning techniques in tasks such as image classification …
Detection of COVID-19 findings by the local interpretable model-agnostic explanations method of types-based activations extracted from CNNs
Covid-19 is a disease that affects the upper and lower respiratory tract and has fatal
consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets …
consequences in individuals. Early diagnosis of COVID-19 disease is important. Datasets …
Augmented Grad-CAM++: Super-Resolution Saliency Maps for Visual Interpretation of Deep Neural Network
Y Gao, J Liu, W Li, M Hou, Y Li, H Zhao - Electronics, 2023 - mdpi.com
In recent years, deep neural networks have shown superior performance in various fields,
but interpretability has always been the Achilles' heel of deep neural networks. The existing …
but interpretability has always been the Achilles' heel of deep neural networks. The existing …
Augmented Score-CAM: High resolution visual interpretations for deep neural networks
There is an increasing demand to understand how neural networks make decisions when
classifying images. Recent deep learning models have a black box architecture that limits …
classifying images. Recent deep learning models have a black box architecture that limits …
Toward explainable artificial intelligence: A survey and overview on their intrinsic properties
JX Mi, X Jiang, L Luo, Y Gao - Neurocomputing, 2024 - Elsevier
Artificial intelligence and its derivative technologies are not only playing a role in the fields of
medicine, economy, policing, transportation, and natural science computing today but also …
medicine, economy, policing, transportation, and natural science computing today but also …
DORA: Exploring outlier representations in deep neural networks
Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal
representations. However, the concepts they learn remain opaque, a problem that becomes …
representations. However, the concepts they learn remain opaque, a problem that becomes …
Perception visualization: Seeing through the eyes of a DNN
Artificial intelligence (AI) systems power the world we live in. Deep neural networks (DNNs)
are able to solve tasks in an ever-expanding landscape of scenarios, but our eagerness to …
are able to solve tasks in an ever-expanding landscape of scenarios, but our eagerness to …
Finding Spurious Correlations with Function-Semantic Contrast Analysis
In the field of Computer Vision (CV), the degree to which two objects, eg two classes, share
a common conceptual meaning, known as semantic similarity, is closely linked to the visual …
a common conceptual meaning, known as semantic similarity, is closely linked to the visual …
Sess: Saliency enhancing with scaling and sliding
High-quality saliency maps are essential in several machine learning application areas
including explainable AI and weakly supervised object detection and segmentation. Many …
including explainable AI and weakly supervised object detection and segmentation. Many …