Research has expanded our understanding of causal relations, links with historic data, and abilities to measure and model climate change. Since the 1990s, scientific research on climate change has included multiple disciplines and has expanded. By the 1990s, as the result of improving the fidelity of computer models and observational work confirming the Milankovitch theory of the ice ages, a consensus position formed: greenhouse gases were deeply involved in most climate changes and human-caused emissions were bringing discernible global warming. Some scientists also pointed out that human activities that generated atmospheric aerosols (e.g., "pollution") could have cooling effects as well.ĭuring the 1970s, scientific opinion increasingly favored the warming viewpoint. In the 1960s, the evidence for the warming effect of carbon dioxide gas became increasingly convincing. Many other theories of climate change were advanced, involving forces from volcanism to solar variation. In the late 19th century, scientists first argued that human emissions of greenhouse gases could change Earth's energy balance and climate. Interpretable and thus advance scientific understanding of brain disorders.The history of the scientific discovery of climate change began in the early 19th century when ice ages and other natural changes in paleoclimate were first suspected and the natural greenhouse effect was first identified. Steer our future research directions to make deep learning models substantially Finally, we discuss the limitations of theĬurrent practices and offer some valuable insights and guidance on how we can Interpretability to capture anatomical and functional brain alterations most Secondly, we discuss how multiple recent neuroimaging studies leveraged model We summarize the current status of interpretability resources in general,įocusing on the progression of methods, associated challenges, and opinions. Reviews interpretable deep learning models in the neuroimaging domain. Method reveals and how to validate its reliability. Researchers are still unclear about what aspect of model learning a post hoc While the interpretability domain is advancing noticeably, Safety-critical domains such as healthcare, finance, and law enforcementĪgencies. Intuitions of how the models reached the decisions, which is essential for In recent years,Įxplainable AI (XAI) has undergone a surge of developments mainly to get Learning models still exist because of the lack of transparency in these modelsįor their successful deployment in real-world applications. Over traditional machine learning algorithms. Neuroimaging studies have also witnessed a noticeable performance advancement Of a separate error-prone feature extraction phase. Mahfuzur Rahman and 2 other authors Download PDF Abstract: Deep learning (DL) models have been popular due to their ability to learnĭirectly from the raw data in an end-to-end paradigm, alleviating the concern Download a PDF of the paper titled Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey, by Md.
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