Youyi Song

Overlapping cytoplasms segmentation via constrained multi-shape evolution for cervical cancer screening

Segmenting overlapping cytoplasms in cervical smear images is a clinically essential task for quantitatively measuring cell-level features to screen cervical cancer This task, however, remains rather challenging, mainly due to the deficiency of intensity (or color) information in the overlapping region Although shape prior-based models that compensate intensity deficiency by introducing prior shape information about cytoplasm are firmly established, they often yield visually implausible results, as they model shape priors only by limited shape hypotheses about cytoplasm, exploit cytoplasm-level shape priors alone, and impose no shape constraint on the resulting shape of the cytoplasm In this paper, we present an effective shape prior-based approach, called constrained multi-shape evolution, that segments all overlapping cytoplasms in the clump simultaneously by jointly evolving each cytoplasm's shape guided by the modeled shape priors We model local shape priors (cytoplasm-level) by an infinitely large shape hypothesis set which contains all possible shapes of the cytoplasm In the shape evolution, we compensate intensity deficiency for the segmentation by introducing not only the modeled local shape priors but also global shape priors (clump-level) modeled by considering mutual shape constraints of cytoplasms in the clump We also constrain the resulting shape in each evolution to be in the built shape hypothesis set for further reducing implausible segmentation results We evaluated the proposed method in two typical cervical smear datasets, and the extensive experimental results confirm its effectiveness.

Cell classification with worse-case boosting for intelligent cervical cancer screening

Cell classification underpins intelligent cervical cancer screening, a cytology examination that effectively decreases both the morbidity and mortality of cervical cancer. This task, however, is rather challenging, mainly due to the difficulty of collecting a training dataset representative sufficiently of the unseen test data, as there are wide variations of cells' appearance and shape at different cancerous statuses. This difficulty makes the classifier, though trained properly, often classify wrongly for cells that are underrepresented by the training dataset, eventually leading to a wrong screening result. To address it, we propose a new learning algorithm, called worse-case boosting, for classifiers effectively learning from under-representative datasets in cervical cell classification. The key idea is to learn more from worse-case data for which the classifier has a larger gradient norm compared to other training data, so these data are more likely to correspond to underrepresented data, by dynamically assigning them more training iterations and larger loss weights for boosting the generalizability of the classifier on underrepresented data. We achieve this idea by sampling worse-case data per the gradient norm information and then enhancing their loss values to update the classifier. We demonstrate the effectiveness of this new learning algorithm on two publicly available cervical cell classification datasets (the two largest ones to the best of our knowledge), and positive results (4% accuracy improvement) yield in the extensive experiments. The source codes are available at: https://github.com/YouyiSong/Worse-Case-Boosting.