Artificial Intelligence in Surgery: Neural Networks and Deep Learning

• URL: http://arxiv.org/abs/2009.13411v1
• Date: Mon, 28 Sep 2020 15:25:00 GMT
• Title: Artificial Intelligence in Surgery: Neural Networks and Deep Learning
• Authors: Deepak Alapatt and Pietro Mascagni, Vinkle Srivastav, Nicolas Padoy
• Abstract summary: 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.
• Score: 2.562741561534933
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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. However, surgeons and computer scientists should partner to develop and assess deep learning applications of value to patients and healthcare systems. This chapter and the accompanying hands-on material were designed for surgeons willing to understand the intuitions behind neural networks, become familiar with deep learning concepts and tasks, grasp what implementing a deep learning model in surgery means, and finally appreciate the specific challenges and limitations of deep neural networks in surgery. For the associated hands-on material, please see https://github.com/CAMMA-public/ai4surgery.

Related papers

• Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks [74.3099028063756]
  We develop a new method with neuronal operations based on lateral connections and Hebbian learning. We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities. Our method consistently solves for spiking neural networks with nearly zero forgetting.
  arXiv  Detail & Related papers  (2024-02-19T09:29:37Z)
• 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)
• Neuronal Auditory Machine Intelligence (NEURO-AMI) In Perspective [0.0]
  We present an overview of a new competing bio-inspired continual learning neural tool Neuronal Auditory Machine Intelligence (Neuro-AMI) In this report, we present an overview of a new competing bio-inspired continual learning neural tool Neuronal Auditory Machine Intelligence (Neuro-AMI)
  arXiv  Detail & Related papers  (2023-10-14T13:17:58Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• In the realm of hybrid Brain: Human Brain and AI [0.0]
  Current brain-computer interface (BCI) technology is mainly on therapeutic outcomes. Recently, artificial intelligence (AI) and machine learning (ML) technologies have been used to decode brain signals. We envision the development of closed loop, intelligent, low-power, and miniaturized neural interfaces.
  arXiv  Detail & Related papers  (2022-10-04T08:35:34Z)
• Neuro-Symbolic Learning of Answer Set Programs from Raw Data [54.56905063752427]
  Neuro-Symbolic AI aims to combine interpretability of symbolic techniques with the ability of deep learning to learn from raw data. We introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data. NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency.
  arXiv  Detail & Related papers  (2022-05-25T12:41:59Z)
• Deep Learning and Artificial General Intelligence: Still a Long Way to Go [0.15229257192293197]
  Deep learning using neural network architecture has been on the frontier of computer science research. This article critically shows five major reasons why deep neural networks, as of the current state, are not ready to be the technique of choice for reaching Artificial General Intelligence.
  arXiv  Detail & Related papers  (2022-03-25T23:36:17Z)
• Interpretability of Neural Network With Physiological Mechanisms [5.1971653175509145]
  Deep learning continues to play as a powerful state-of-art technique that has achieved extraordinary accuracy levels in various domains of regression and classification tasks. The original goal of proposing the neural network model is to improve the understanding of complex human brains using a mathematical expression approach. Recent deep learning techniques continue to lose the interpretations of its functional process by being treated mostly as a black-box approximator.
  arXiv  Detail & Related papers  (2022-03-24T21:40:04Z)
• 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 learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI [2.0178765779788495]
  Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research. In the last decade, deep learning has become the state-of-the-art method in many machine learning tasks. Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks.
  arXiv  Detail & Related papers  (2020-05-19T18:10:35Z)
• 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)

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.