Rapid slide-level cytopathology diagnosis enabled by a multicamera array scanner (MCAS)

Developing digital pathology solutions for thick cytology specimens such as FNA smears is challenging because of their large area and 3D nature. Additionally, traditional deep learning methods for computational pathology require manual annotation of whole slide images, which is labor-intensive and time-consuming. To image thick FNA smears quickly, we have developed a multi-camera array scanner (MCAS) that digitizes entire cytology slides in 3D within 4 minutes. Here, we present a multiple-instance learning-based weakly-supervised deep learning method that predicts the presence of adenocarcinoma from the whole-slide images obtained by the MCAS using only slide-level diagnosis label for training, Our method also locates the regions with the highest contribution to the algorithm decision, thus enhancing interpretability. Given the speed of scanning and the interpretability that our method offers, it can serve as a very helpful screening tool for pathologists, increasing the speed of decision-making.