6 research outputs found
A multi-centre polyp detection and segmentation dataset for generalisability assessment
Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp’s number, size and surface structure are linked to the risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation
Enscript Output
Abstract This document describes a modified sender-side algorithm for managing the TCP and Stream Control Transmission Protocol (SCTP) retransmission timers that provides faster loss recovery when there is a small amount of outstanding data for a connection. The modification, RTO Restart (RTOR), allows the transport to restart its retransmission timer using a smaller timeout duration, so that the effective retransmission timeout (RTO) becomes more aggressive in situations where fast retransmit cannot be used. This enables faster loss detection and recovery for connections that are short lived or application limited
PolypGen: A multi-center polyp detection and segmentation dataset for generalisability assessment
Polyps in the colon are widely known as cancer precursors identified by
colonoscopy either related to diagnostic work-up for symptoms, colorectal
cancer screening or systematic surveillance of certain diseases. Whilst most
polyps are benign, the number, size and the surface structure of the polyp are
tightly linked to the risk of colon cancer. There exists a high missed
detection rate and incomplete removal of colon polyps due to the variable
nature, difficulties to delineate the abnormality, high recurrence rates and
the anatomical topography of the colon. In the past, several methods have been
built to automate polyp detection and segmentation. However, the key issue of
most methods is that they have not been tested rigorously on a large
multi-center purpose-built dataset. Thus, these methods may not generalise to
different population datasets as they overfit to a specific population and
endoscopic surveillance. To this extent, we have curated a dataset from 6
different centers incorporating more than 300 patients. The dataset includes
both single frame and sequence data with 3446 annotated polyp labels with
precise delineation of polyp boundaries verified by six senior
gastroenterologists. To our knowledge, this is the most comprehensive detection
and pixel-level segmentation dataset curated by a team of computational
scientists and expert gastroenterologists. This dataset has been originated as
the part of the Endocv2021 challenge aimed at addressing generalisability in
polyp detection and segmentation. In this paper, we provide comprehensive
insight into data construction and annotation strategies, annotation quality
assurance and technical validation for our extended EndoCV2021 dataset which we
refer to as PolypGen.Comment: 15 page