Multiclass deep active learning for detecting red blood cell subtypes in brightfield microscopy

Ario Sadafi, Niklas Koehler, Asya Makhro, Anna Bogdanova, Nassir Navab, Carsten Marr, Tingying Peng

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

21 Zitate (Scopus)

Abstract

The recent success of deep learning approaches relies partly on large amounts of well annotated training data. For natural images object annotation is easy and cheap. For biomedical images however, annotation crucially depends on the availability of a trained expert whose time is typically expensive and scarce. To ensure efficient annotation, only the most relevant objects should be presented to the expert. Currently, no approach exists that allows to select those for a multiclass detection problem. Here, we present an active learning framework that identifies the most relevant samples from a large set of not annotated data for further expert annotation. Applied to brightfield images of red blood cells with seven subtypes, we train a faster R-CNN for single cell identification and classification, calculate a novel confidence score using dropout variational inference and select relevant images for annotation based on (i) the confidence of the single cell detection and (ii) the rareness of the classes contained in the image. We show that our approach leads to a drastic increase of prediction accuracy with already few annotated images. Our original approach improves classification of red blood cell subtypes and speeds up the annotation. This important step in diagnosing blood diseases will profit from our framework as well as many other clinical challenges that suffer from the lack of annotated training data.

OriginalspracheEnglisch
TitelMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Redakteure/-innenDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten685-693
Seitenumfang9
ISBN (Print)9783030322380
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Dauer: 13 Okt. 201917 Okt. 2019

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band11764 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Land/GebietChina
OrtShenzhen
Zeitraum13/10/1917/10/19

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