Abstract:
With the Artificial Intelligence becoming more and more powerful in time, there is
a growing interest about scientists focusing in providing answers in the field of medical
image analysis. The importance of using AI in the medical field is of great significance for
many reasons. Robust algorithms are able to help physicians in processing large amounts
of data, assisting in providing diagnosis and mitigating human error while reducing time
and cost. This is especially applicable in the case of cell image analysis. Considering the
complexity of cells as entities, which derives from their shape and form, it can be quite
challenging to detect them in different settings that they are placed. An automated process
on cell detection will be very beneficiary for anatomopathologists. In this work, there is
cell detection performed, using the state-of-the-art deep learning object detection model,
Faster R-CNN and trained on hand-crafted features such as Local Binary Patterns. The
dataset used portrayed several challenges that most microscopy images hold, which
required histogram and template matching for preprocessing. With cell detection
performed, we also count the cells in an image by nuclei localization. There were several
experiments conducted that achieve up to 56% mAP.
