PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep
  Learning-Based Centroblast Cell Detection
Narongrid Seesawad Piyalitt Ittichaiwong Thapanun Sudhawiyangkul Phattarapong Sawangjai Peti Thuwajit Paisarn Boonsakan Supasan Sripodok Kanyakorn Veerakanjana Phoomraphee Luenam Komgrid Charngkaew Ananya Pongpaibul Napat Angkathunyakul Narit Hnoohom Sumeth Yuenyong Chanitra Thuwajit Theerawit Wilaiprasitporn
Patch classification models based on deep learning have been utilized in
whole-slide images (WSI) of H&E-stained tissue samples to assist pathologists
in grading follicular lymphoma patients. However, these approaches still
require pathologists to manually identify centroblast cells and provide refined
labels for optimal performance. To address this, we propose PseudoCell, an
object detection framework to automate centroblast detection in WSI (source
code is available at https://github.com/IoBT-VISTEC/PseudoCell.git). This
framework incorporates centroblast labels from pathologists and combines them
with pseudo-negative labels obtained from undersampled false-positive
predictions using the cell's morphological features. By employing PseudoCell,
pathologists' workload can be reduced as it accurately narrows down the areas
requiring their attention during examining tissue. Depending on the confidence
threshold, PseudoCell can eliminate 58.18-99.35% of non-centroblasts tissue
areas on WSI. This study presents a practical centroblast prescreening method
that does not require pathologists' refined labels for improvement. Detailed
guidance on the practical implementation of PseudoCell is provided in the
discussion section.