Title
Decoupled gradient harmonized detector for partial annotation: Application to signet ring cell detection
Abstract
Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients. Due to lack of public dataset and expert-level annotations, automatic detection on signet ring cell (SRC) has not been thoroughly investigated. In MICCAI DigestPath2019 challenge, apart from foreground (SRC region)-background (normal tissue area) class imbalance, SRCs are partially annotated due to costly medical image annotation, which introduces extra label noise. To address the issues simultaneously, we propose Decoupled Gradient Harmonizing Mechanism (DGHM) and embed it into classification loss, denoted as DGHM-C loss. Specifically, besides positive (SRCs) and negative (normal tissues) examples, we further decouple noisy examples from clean examples and harmonize the corresponding gradient distributions in classification respectively. Without whistles and bells, we achieved the 2nd place in the challenge. Ablation studies and controlled label missing rate experiments demonstrate that DGHM-C loss can bring substantial improvement in partially annotated object detection.
Year
DOI
Venue
2021
10.1016/j.neucom.2020.03.128
Neurocomputing
Keywords
DocType
Volume
Computer-aided detection,Signet ring cell,Partially annotated object detection,DGHM
Journal
453
ISSN
Citations 
PageRank 
0925-2312
1
0.36
References 
Authors
0
5
Name
Order
Citations
PageRank
Tiancheng Lin151.49
Guo Yuanfan210.36
Yang Canqian311.38
Jiancheng Yang4206.74
Yi Xu563.73