  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.
