• Corpus ID: 239616447

Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations

@article{Hong2021RethinkingGP,
  title={Rethinking Generalization Performance of Surgical Phase Recognition with Expert-Generated Annotations},
  author={Seungbum Hong and Jiwon Lee and Bokyung Park and Ahmed A. Alwusaibie and Anwar H. Alfadhel and Sunghyun Park and Woo Jin Hyung and Min-Kook Choi},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.11626}
}
As the area of application of deep neural networks expands to areas requiring expertise, e.g., in medicine and law, more exquisite annotation processes for expert knowledge training are required. In particular, it is difficult to guarantee generalization performance in the clinical field in the case of expert knowledge training where opinions may differ even among experts on annotations. To raise the issue of the annotation generation process for expertise training of CNNs, we verified the… 
View PDF on arXiv

References

SHOWING 1-10 OF 36 REFERENCES

Less is More: Surgical Phase Recognition with Less Annotations through Self-Supervised Pre-training of CNN-LSTM Networks

This work presents a new self-supervised pre-training approach based on the prediction of remaining surgery duration (RSD) from laparoscopic videos that outperforms the single pre- Training approach for surgical phase recognition presently published in the literature and observes that end-to-end training of CNN-LSTM networks boosts surgicalphase recognition performance.

Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition

This work confronts the problem of learning surgical phase recognition in scenarios presenting scarce amounts of annotated data and proposes a teacher/student type of approach, where a strong predictor called the teacher, trained beforehand on a small dataset of ground truth-annotated videos, generates synthetic annotations for a larger dataset, which another model - the student - learns from.

Real-time automatic surgical phase recognition in laparoscopic sigmoidectomy using the convolutional neural network-based deep learning approach

A deep learning model based on laparoscopic sigmoidectomy (Lap-S) videos, which could be used for real-time phase recognition, and to clarify the accuracies of the automatic surgical phase and action recognitions using visual information is developed.

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.

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.

Surgical Phase Recognition of Short Video Shots Based on Temporal Modeling of Deep Features

This paper investigates four state-of-the-art CNN architectures (Alexnet, VGG19, GoogleNet, and ResNet101), for feature extraction via transfer learning and investigates the role of 'elapsed time' (from the beginning of the operation), and shows that inclusion of this feature can increase performance dramatically.

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning

RIL-Contour and the AID methodology accelerate dataset annotation and model development by facilitating rapid collaboration between analysts, radiologists, and engineers.

An overview of deep learning in medical imaging focusing on MRI

Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?

Whether current video datasets have sufficient data for training very deep convolutional neural networks with spatio-temporal three-dimensional (3D) kernels is determined and it is believed that using deep 3D CNNs together with Kinetics will retrace the successful history of 2DCNNs and ImageNet, and stimulate advances in computer vision for videos.

Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks

Modelling approaches performed to automatically classify and annotate radiographs using several classification schemes, which can be further applied for automatic content-based image retrieval (CBIR) and computer-aided diagnosis (CAD).