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Early diagnosis of signet ring cell carcinoma dramatically improves thesurvival rate of patients. Due to lack of public dataset and expert-levelannotations, automatic detection on signet ring cell (SRC) has not beenthoroughly investigated. In MICCAI DigestPath2019 challenge, apart fromforeground (SRC region)-background (normal tissue area) class imbalance, SRCsare partially annotated due to costly medical image annotation, whichintroduces extra label noise. To address the issues simultaneously, we proposeDecoupled Gradient Harmonizing Mechanism (DGHM) and embed it intoclassification loss, denoted as DGHM-C loss. Specifically, besides positive(SRCs) and negative (normal tissues) examples, we further decouple noisyexamples from clean examples and harmonize the corresponding gradientdistributions in classification respectively. Without whistles and bells, weachieved the 2nd place in the challenge. Ablation studies and controlled labelmissing rate experiments demonstrate that DGHM-C loss can bring substantialimprovement in partially annotated object detection.

Decoupled Gradient Harmonized Detector for Partial Annotation: Application to Signet Ring Cell Detection