Artifact Augmentation for Enhanced Tissue Detection in Microscope Scanner Systems

Abstract

As the field of routine pathology transitions into the digital realm, there is a surging demand for the full automation of microscope scanners, aiming to expedite the process of digitizing tissue samples, and consequently, enhancing the efficiency of case diagnoses. The key to achieving seamless automatic imaging lies in the precise detection and segmentation of tissue sample regions on the glass slides. State-of-the-art approaches for this task lean heavily on deep learning techniques, particularly U-Net convolutional neural networks. However, since samples can be highly diverse and prepared in various ways, it is almost impossible to be fully prepared for and cover every scenario with training data. We propose a data augmentation step that allows artificially modifying the training data by extending some artifact features of the available data to the rest of the dataset. This procedure can be used to generate images that can be considered synthetic. These artifacts could include felt pen markings, speckles of dirt, residual bubbles in covering glue, or stains. The proposed approach achieved a 1–6% improvement for these samples according to the F1 Score metric.

Blood Cell Attribute Classification Algorithm Based on Partial Label Learning

“…In recent years, with the improvement of hardware performance and advancements in deep learning technologies, numerous sophisticated deep learning models have emerged [5][6][7][8][9], particularly excelling in the medical field [10,11]. Undeniably, deep learning has gained a distinct advantage over traditional machine learning methods in blood cell image classification [12], leading many scholars to apply deep learning in the processing of blood cell images [13][14][15].…”