Deep Learning for Epidemiologists: An Introduction to Neural Networks

• URL: http://arxiv.org/abs/2202.01319v1
• Date: Wed, 2 Feb 2022 22:52:18 GMT
• Title: Deep Learning for Epidemiologists: An Introduction to Neural Networks
• Authors: Stylianos Serghiou, Kathryn Rough
• Abstract summary: Article introduces the fundamentals of deep learning from an epidemiological perspective. We aim to enable the reader to engage with and critically evaluate medical applications of deep learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning methods are increasingly being applied to problems in medicine and healthcare. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces to the fundamentals of deep learning from an epidemiological perspective. Specifically, this article reviews core concepts in machine learning (overfitting, regularization, hyperparameters), explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks), and summarizes training, evaluation, and deployment of models. We aim to enable the reader to engage with and critically evaluate medical applications of deep learning, facilitating a dialogue between computer scientists and epidemiologists that will improve the safety and efficacy of applications of this technology.

Related papers

• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• Review of deep learning in healthcare [0.0]
  This research examines deep learning methods used in healthcare systems via an examination of cutting-edge network designs, applications, and market trends. The initial objective is to provide in-depth insight into the deployment of deep learning models in healthcare solutions. And last, to outline the current unresolved issues and potential directions.
  arXiv  Detail & Related papers  (2023-10-01T16:58:20Z)
• Deep Learning in Deterministic Computational Mechanics [0.0]
  This review focuses on deep learning methods rather than applications for computational mechanics. The primary audience is researchers at the verge of entering this field or those who attempt to gain an overview of deep learning in computational mechanics.
  arXiv  Detail & Related papers  (2023-09-27T05:57:19Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Ten Quick Tips for Deep Learning in Biology [116.78436313026478]
  Machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Deep learning has become its own subfield of machine learning. In the context of biological research, deep learning has been increasingly used to derive novel insights from high-dimensional biological data.
  arXiv  Detail & Related papers  (2021-05-29T21:02:44Z)
• Deep Recurrent Encoder: A scalable end-to-end network to model brain signals [122.1055193683784]
  We propose an end-to-end deep learning architecture trained to predict the brain responses of multiple subjects at once. We successfully test this approach on a large cohort of magnetoencephalography (MEG) recordings acquired during a one-hour reading task.
  arXiv  Detail & Related papers  (2021-03-03T11:39:17Z)
• Learning Contact Dynamics using Physically Structured Neural Networks [81.73947303886753]
  We use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations. Our results indicate that an idealised form of touch feedback is a key component of making this learning problem tractable.
  arXiv  Detail & Related papers  (2021-02-22T17:33:51Z)
• Artificial Intelligence in Surgery: Neural Networks and Deep Learning [2.562741561534933]
  Deep neural networks power most recent successes of artificial intelligence, spanning from self-driving cars to computer aided diagnosis in radiology and pathology. The high-stake data intensive process of surgery could highly benefit from such computational methods. This chapter and the accompanying hands-on material were designed for surgeons willing to understand the intuitions behind neural networks.
  arXiv  Detail & Related papers  (2020-09-28T15:25:00Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)
• On Interpretability of Artificial Neural Networks: A Survey [21.905647127437685]
  We systematically review recent studies in understanding the mechanism of neural networks, describe applications of interpretability especially in medicine. We discuss future directions of interpretability research, such as in relation to fuzzy logic and brain science.
  arXiv  Detail & Related papers  (2020-01-08T13:40:42Z)
• Deep neural network models for computational histopathology: A survey [1.2891210250935146]
  deep learning has become the mainstream methodological choice for analyzing and interpreting cancer histology images. In this paper, we present a comprehensive review of state-of-the-art deep learning approaches that have been used. We highlight critical challenges and limitations with current deep learning approaches, along with possible avenues for future research.
  arXiv  Detail & Related papers  (2019-12-28T01:04:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.