Search Results for author:
Found 11 papers, 7 papers with code
SegMatch: A semi-supervised learning method for surgical instrument segmentation
Surgical instrument segmentation is recognised as a key enabler to provide advanced surgical assistance and improve computer assisted interventions.
LoViT: Long Video Transformer for Surgical Phase Recognition
Our results demonstrate the effectiveness of our approach in achieving state-of-the-art performance of surgical phase recognition on two datasets of different surgical procedures and temporal sequencing characteristics whilst introducing mechanisms that cope with long videos.
Ranked #1 on
Surgical phase recognition
on Cholec80
Hyperspectral Image Segmentation: A Preliminary Study on the Oral and Dental Spectral Image Database (ODSI-DB)
Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks.
VideoSum: A Python Library for Surgical Video Summarization
It is thus unsurprising that substantial research efforts are made to develop methods aiming at mitigating the scarcity of annotated SDS data.
Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset
The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines.
Intrapapillary Capillary Loop Classification in Magnification Endoscopy: Open Dataset and Baseline Methodology
A future work path of high clinical relevance is the extension of the classification to ESCN types.
Image Compositing for Segmentation of Surgical Tools without Manual Annotations
Background data is captured by collecting videos that do not contain instruments.
Interpretable Fully Convolutional Classification of Intrapapillary Capillary Loops for Real-Time Detection of Early Squamous Neoplasia
We present a new approach to visualise attention that aims to give some insights on those areas of the oesophageal tissue that lead a network to conclude that the images belong to a particular class and compare them with those visual features employed by clinicians to produce a clinical diagnosis.
Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation using Holistic Convolutional Networks
3) We show that the joint use of holistic CNNs and generalised Wasserstein Dice scores achieves segmentations that are more semantically meaningful for brain tumour segmentation.
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization ability of the design and maintain the segmentation accuracy without overfitting the training sets.
Scalable multimodal convolutional networks for brain tumour segmentation
Brain tumour segmentation plays a key role in computer-assisted surgery.
