• Corpus ID: 221970166

Artificial Intelligence in Surgery: Neural Networks and Deep Learning

  title={Artificial Intelligence in Surgery: Neural Networks and Deep Learning},
  author={Deepak Alapatt and Pietro Mascagni and Vinkle Kumar Srivastav and Nicolas Padoy},
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… 


Machine and deep learning for workflow recognition during surgery
  • N. Padoy
  • Computer Science
    Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
  • 2019
It is presented here how several recent techniques relying on machine and deep learning can be used to analyze the activities taking place during surgery, using videos captured from either endoscopic or ceiling-mounted cameras.
Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning
The results suggest that automatic surgical activity recognition during RARP is feasible and can be the foundation for advanced analytics, and RP-Net, a modified version of InceptionV3 model, out-performs all other RNN and CNN based models explored in this paper.
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
  • Amy Jin, S. Yeung, Li Fei-Fei
  • Computer Science, Medicine
    2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
  • 2018
This work introduces an approach to automatically assess surgeon performance by tracking and analyzing tool movements in surgical videos, leveraging region-based convolutional neural networks, and is the first to not only detect presence but also spatially localize surgical tools in real-world laparoscopic surgical videos.
CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions
Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors, and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human–AI actor team.
EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos
This paper proposes a novel method for phase recognition that uses a convolutional neural network (CNN) to automatically learn features from cholecystectomy videos and that relies uniquely on visual information.
Multitask Learning of Temporal Connectionism in Convolutional Networks using a Joint Distribution Loss Function to Simultaneously Identify Tools and Phase in Surgical Videos
A multi-task learning framework using CNN followed by a bi-directional long short term memory (Bi-LSTM) to learn to encapsulate both forward and backward temporal dependencies is proposed.
Cerebrovascular Network Segmentation of MRA Images With Deep Learning
This work presents a convolutional neural network approach for segmentation of the cerebrovascular structure from magnetic resonance angiography inspired by the U-net 3D and by the Inception modules, entitled Uception.
U-Net: Convolutional Networks for Biomedical Image Segmentation
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.