来自用户 尹志 的文献。
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41.
尹志
(2022-01-31 12:53):
#paper doi:10.1038/nature14539 LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015). 这是深度学习三巨头于2015年写的一篇nature综述。也是nature纪念AI60周年的一系列综述paper里的一篇。这篇paper综述了深度学习这一热门主题。当然，作为深度学习的几位奠基人，确实把深度学习的概念原理应用写的深入浅出。本文从监督学习一直介绍到反向传播，主要综述了CNN和RNN的原理及其应用，很适合初学者全面了解（当时）的深度学习的概貌。在最后一段深度学习的未来一节，作者对无监督学习的未来报以热烈的期望，看看这几年，特别是yann lecun大力推动的自监督成为显学，也算是念念不忘必有回响了。
Abstract:
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in …
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Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
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42.
尹志
(2022-01-18 23:37):
#paper doi:10.1038/s41416-020-01122-x Deep learning in cancer pathology: a new generation of clinical biomarkers. British Journal of Cancer, 2020 Nov 18. 这是一篇综述，综述了一下深度学习从病理图像直接抽取biomarker的相关概念，以及病理图像中利用深度学习做的各种基本的和进阶的图像分析任务。
我们知道，在肿瘤的临床治疗中会基于各种分子生物标记物。但这些分子标记物都比较耗时费力。而且一般而言，这些分子标记物都需要tumour tissue。 但其实tumour tissue上有很多信息我们现在都没好好利用。利用深度学习，我们可以直接从常规病理图像中提取更多信息。从而提供潜在的具有临床价值的信息。
里面介绍的基本任务包括：检测、评级、tumour tissue亚型预测。这些任务的目的是自动化病理诊断流程，但结论不形成直接的临床决策。（辅助诊断呗）。
进阶任务可直接影响临床决策：比如分子特性推断、生存率预测、端到端的疗效预测。这些任务都可以直接影响临床决策，但目前需要更好的临床验证。比如需要更多前瞻性实验的验证。（就是还不能用呗）。
Abstract:
Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine …
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Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers. However, the growing number of these complex biomarkers tends to increase the cost and time for decision-making in routine daily oncology practice; furthermore, biomarkers often require tumour tissue on top of routine diagnostic material. Nevertheless, routinely available tumour tissue contains an abundance of clinically relevant information that is currently not fully exploited. Advances in deep learning (DL), an artificial intelligence (AI) technology, have enabled the extraction of previously hidden information directly from routine histology images of cancer, providing potentially clinically useful information. Here, we outline emerging concepts of how DL can extract biomarkers directly from histology images and summarise studies of basic and advanced image analysis for cancer histology. Basic image analysis tasks include detection, grading and subtyping of tumour tissue in histology images; they are aimed at automating pathology workflows and consequently do not immediately translate into clinical decisions. Exceeding such basic approaches, DL has also been used for advanced image analysis tasks, which have the potential of directly affecting clinical decision-making processes. These advanced approaches include inference of molecular features, prediction of survival and end-to-end prediction of therapy response. Predictions made by such DL systems could simplify and enrich clinical decision-making, but require rigorous external validation in clinical settings.
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