Solution to overcome the sparsity issue of annotated data in medical domain

Annotations are critical for machine learning and developing computer aided diagnosis (CAD) algorithms. Good performance of CAD is critical to their adoption, which generally rely on training with a wide variety of annotated data. However, a vast amount of medical data is either unlabeled or annotat...

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Bibliographic Details
Published in:	CAAI Transactions on Intelligence Technology Vol. 3; no. 3; pp. 153 - 160
Main Authors:	Pujitha, Appan K, Sivaswamy, Jayanthi
Format:	Journal Article
Language:	English
Published:	Beijing The Institution of Engineering and Technology 01.09.2018 John Wiley & Sons, Inc Wiley
Subjects:	(B6135) Optical, image and video signal processing (C5260B) Computer vision and image processing techniques (C6170K) Knowledge engineering techniques (C7330) Biology and medical computing Algorithms annotated data Annotations Artificial neural networks CAD colour fundus images Computer aided design crowdsourced annotations crowdsourced region Crowdsourcing data augmentation data-driven approaches Deep learning detection performance developing computer diagnosis algorithms diseases Exudation generative adversarial network-based solution Generative adversarial networks good performance image classification image colour analysis image level Image processing image segmentation interest markings learning (artificial intelligence) Lesions Machine learning Magnetic resonance imaging medical data medical domain medical image processing Medical imaging Network reliability neural nets Neural networks pixel-level markings popular solution processed/refined crowdsourced data/synthetic images Retinal images severity level Sparsity Special Issue: Selected Papers from The 4th Asian Conference on Pattern Recognition (ACPR 2017) Synthetic data synthetic image generation synthetically generated data diagnosis algorithms image classification data augmentation popular solution detection performance annotated data synthetically generated data deep learning medical domain crowdsourced region image level interest markings image segmentation processed/refined crowdsourced data/synthetic images image colour analysis learning (artificial intelligence) medical image processing generative adversarial network-based solution CAD diseases good performance colour fundus images crowdsourced annotations machine learning severity level data-driven approaches synthetic image generation medical data pixel-level markings neural nets developing computer
ISSN:	2468-2322, 2468-6557, 2468-2322
Online Access:	Get full text
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