Automatic image and text-based description for colorectal polyps using BASIC classification.
Colorectal polyps (CRP) are precursor lesions of colorectal cancer (CRC). Correct identification of CRPs during in-vivo colonoscopy is supported by the endoscopist's expertise and medical classification models. A recent developed classification model is the Blue light imaging Adenoma Serrated Intern...
| Published in: | Artificial Intelligence in Medicine Vol. 121 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | research Journal Article |
| Published: |
Elsevier B.V.
Nov2021
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153477360&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153477360 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Nov2021 vid: 121 pid: 1004 pub: Elsevier B.V. artinfo: ui: 153477360 153477360 NLM34763800 153477360 10.1016/j.artmed.2021.102178 NLM34763800 153477360 ppct: 1 formats: tig: atl: Automatic image and text-based description for colorectal polyps using BASIC classification. aug: au: Fonollà, Roger van der Zander, Quirine E.W. Schreuder, Ramon M. Subramaniam, Sharmila Bhandari, Pradeep Masclee, Ad A.M. Schoon, Erik J. van der Sommen, Fons de With, Peter H.N. affil: Department of Electrical Engineering, Video Coding and Architectures (VCA), Eindhoven University of Technology, Eindhoven, Noord-Brabant, the Netherlands sug: subj: Colorectal Neoplasms Colonic Polyps Adenoma Light Human Colonoscopy Comparative Studies Multicenter Studies Evaluation Research Validation Studies Short Portable Mental Status Questionnaire Clinical Assessment Tools Scales ab: Colorectal polyps (CRP) are precursor lesions of colorectal cancer (CRC). Correct identification of CRPs during in-vivo colonoscopy is supported by the endoscopist's expertise and medical classification models. A recent developed classification model is the Blue light imaging Adenoma Serrated International Classification (BASIC) which describes the differences between non-neoplastic and neoplastic lesions acquired with blue light imaging (BLI). Computer-aided detection (CADe) and diagnosis (CADx) systems are efficient at visually assisting with medical decisions but fall short at translating decisions into relevant clinical information. The communication between machine and medical expert is of crucial importance to improve diagnosis of CRP during in-vivo procedures. In this work, the combination of a polyp image classification model and a language model is proposed to develop a CADx system that automatically generates text comparable to the human language employed by endoscopists. The developed system generates equivalent sentences as the human-reference and describes CRP images acquired with white light (WL), blue light imaging (BLI) and linked color imaging (LCI). An image feature encoder and a BERT module are employed to build the AI model and an external test set is used to evaluate the results and compute the linguistic metrics. The experimental results show the construction of complete sentences with an established metric scores of BLEU-1 = 0.67, ROUGE-L = 0.83 and METEOR = 0.50. The developed CADx system for automatic CRP image captioning facilitates future advances towards automatic reporting and may help reduce time-consuming histology assessment. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|