Cervical Glandular Cell Detection from Whole Slide Image with Out-Of-Distribution Data

by   Ziquan Wei, et al.

Cervical glandular cell (GC) detection is a key step in computer-aided diagnosis for cervical adenocarcinomas screening. It is challenging to accurately recognize GCs in cervical smears in which squamous cells are the major. Widely existing Out-Of-Distribution (OOD) data in the entire smear leads decreasing reliability of machine learning system for GC detection. Although, the State-Of-The-Art (SOTA) deep learning model can outperform pathologists in preselected regions of interest, the mass False Positive (FP) prediction with high probability is still unsolved when facing such gigapixel whole slide image. This paper proposed a novel PolarNet based on the morphological prior knowledge of GC trying to solve the FP problem via a self-attention mechanism in eight-neighbor. It estimates the polar orientation of nucleus of GC. As a plugin module, PolarNet can guide the deep feature and predicted confidence of general object detection models. In experiments, we discovered that general models based on four different frameworks can reject FP in small image set and increase the mean of average precision (mAP) by 0.007∼0.015 in average, where the highest exceeds the recent cervical cell detection model 0.037. By plugging PolarNet, the deployed C++ program improved by 8.8% on accuracy of top-20 GC detection from external WSIs, while sacrificing 14.4 s of computational time. Code is available in https://github.com/Chrisa142857/PolarNet-GCdet


page 1

page 5

page 11


PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection

Patch classification models based on deep learning have been utilized in...

Progressive Attention Guidance for Whole Slide Vulvovaginal Candidiasis Screening

Vulvovaginal candidiasis (VVC) is the most prevalent human candidal infe...

CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer

Blood cell detection is a typical small-scale object detection problem i...

Automatic label correction based on CCESD

In the computer-aided diagnosis of cervical precancerous lesions, it is ...

Rethinking Mitosis Detection: Towards Diverse Data and Feature Representation

Mitosis detection is one of the fundamental tasks in computational patho...

Exploring Contextual Relationships for Cervical Abnormal Cell Detection

Cervical abnormal cell detection is a challenging task as the morphologi...

Histogram of Cell Types: Deep Learning for Automated Bone Marrow Cytology

Bone marrow cytology is required to make a hematological diagnosis, infl...

Please sign up or login with your details

Forgot password? Click here to reset