This paper — from BIDS Data Scientist Daniella Ushizima and her team at the Center for Recognition and Inspection of Cells (CRIC) — earned the best student paper award for original research in the Artificial Intelligence and Decision Support Systems session of the 21st International Conference on Enterprise Information Systems in Heraklion, Crete, Greece, on May 3, 2019
Abstract: In this work, we propose an Iterated Local Search (ILS) approach to detect cervical cell nuclei from digitized Pap smear slides. The problem consists in finding the best values for the parameters to identify where the cell nuclei are located in the image. This is an important step in building a computational tool to help pathologists to identify cell alterations from Pap tests. Our approach is evaluated by using the ISBI Overlapping Cervical Cytology Image Segmentation Challenge (2014) database, which has 945 synthetic images and their respective ground truth. The precision achieved by the proposed heuristic approach is among the best ones in the literature; however, the recall still needs improvement.