Efficient Medical Image Assessment via Self-supervised Learning

Chun Yin Huang, Qi Lei, Xiaoxiao Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

High-performance deep learning methods typically rely on large annotated training datasets, which are difficult to obtain in many clinical applications due to the high cost of medical image labeling. Existing data assessment methods commonly require knowing the labels in advance, which are not feasible to achieve our goal of ‘knowing which data to label.’ To this end, we formulate and propose a novel and efficient data assessment strategy, EXponentiAl Marginal sINgular valuE (EXAMINE ) score, to rank the quality of unlabeled medical image data based on their useful latent representations extracted via Self-supervised Learning (SSL) networks. Motivated by theoretical implication of SSL embedding space, we leverage a Masked Autoencoder [8] for feature extraction. Furthermore, we evaluate data quality based on the marginal change of the largest singular value after excluding the data point in the dataset. We conduct extensive experiments on a pathology dataset. Our results indicate the effectiveness and efficiency of our proposed methods for selecting the most valuable data to label.

Original languageEnglish (US)
Title of host publicationData Augmentation, Labelling, and Imperfections - 2nd MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsHien V. Nguyen, Sharon X. Huang, Yuan Xue
PublisherSpringer Science and Business Media Deutschland GmbH
Pages102-111
Number of pages10
ISBN (Print)9783031170263
DOIs
StatePublished - 2022
Event2nd MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 22 2022Sep 22 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13567 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period9/22/229/22/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Efficient Medical Image Assessment via Self-supervised Learning'. Together they form a unique fingerprint.

Cite this