A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images

Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose a fully automated pipeline for oral cancer detection on whole slide cytology images. The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification. Our novel focus selection step provides fast per-cell focus decisions at human-level accuracy. We demonstrate that the pipeline provides efficient cancer classification of whole slide cytology images, improving over previous results both in terms of accuracy and feasibility. The complete source code is available at https://github.com/MIDA-group/OralScreen.Comment: Accepted to ICIAR 202

Synergistic activity of polarised osteoblasts inside condensations cause their differentiation

Condensation of pre-osteogenic, or pre-chondrogenic, cells is the first of a series of processes that initiate skeletal development. We present a validated, novel, three-dimensional agent-based model of in vitro intramembranous osteogenic condensation. The model, informed by system heterogeneity and relying on an interaction-reliant strategy, is shown to be sensitive to 'rules' capturing condensation growth and can be employed to track activity of individual cells to observe their macroscopic impact. It, therefore, makes available previously inaccessible data, offering new insights and providing a new context for exploring the emergence, as well as normal and abnormal development, of osteogenic structures. Of the several stages of condensation we investigate osteoblast 'burial' within the osteoid they deposit. The mechanisms underlying entrapment - required for osteoblasts to differentiate into osteocytes - remain a matter of conjecture with several hypotheses claiming to capture this important transition. Computational examination of this transition indicates that osteoblasts neither turn off nor slow down their matrix secreting genes - a widely held view; nor do they secrete matrix randomly. The model further reveals that osteoblasts display polarised behaviour to deposit osteoid. This is both an important addition to our understanding of condensation and an important validation of the model's utility

Supplementary Figure S1 from Negative Predictive Value of Circulating Tumor Tissue Modified Viral (TTMV)-HPV DNA for HPV-driven Oropharyngeal Cancer Surveillance

Supplementary Figure S1: Case examples of false negative TTMV-HPV DNA results identified during surveillance</p