Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue
Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance compared with the conventional method.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A fuzzy distance-based ensemble of deep models for cervical cancer detection
Background and Objective Cervical cancer is one of the leading causes of women’s death. Like any other disease, cervical cancer’s early detection and treatment with the best possible medical advice are the paramount ste…
Image ClassificationTransfer LearningAutomated Cervical Os Segmentation for Camera-Guided, Speculum-Free Screening
Cervical cancer is highly preventable, yet persistent barriers to screening limit progress toward elimination goals. Speculum-free devices that integrate imaging and sampling could improve access, particularly in low-res…
DeepPap: Deep Convolutional Networks for Cervical Cell Classification
Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. How…
ClassificationGeneral ClassificationSpecificityComparison-Based Convolutional Neural Networks for Cervical Cell/Clumps Detection in the Limited Data Scenario
Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the succe…
Cell SegmentationFew-Shot Learningobject-detectionObject DetectionDeep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images
Cervical cancer is a prevalent disease affecting millions of women worldwide every year. It requires significant attention, as early detection during the precancerous stage provides an opportunity for a cure. The screeni…
Deep Learning