FIMIL : A high-throughput deep learning model for abnormality detection with weak annotation in microscopy images

Automatic computer-aided detection plays an important role in biomedical image analysis. Many studies have focused on weak supervised learning as annotation tasks are time-consuming and tedious. Compared with pixel-wise annotation by particular software on the scanned digital high-resolution images, an alternative method of marking out of suspicious regions on microscopy slides is significantly more convenient for pathologists. Additionally, with a focus on dysplasias in the central area, there is a high likelihood of the similar tissues to be found around in clusters. In this paper, for weak annotation on microscopy images, we propose an efficient Foveated Imaging based Multiple Instance Learning (FIMIL) framework to classify weakly-labeled microscopy images. The model also provides multi-scale algorithm for arbitrary image size, in which the patches with highest possibility to contain dysplasia are considered as ”fixation points” in the image. The developed model combines deep convolutional neural networks (CNNs) with multiple instance learning (MIL) for dysplasias detection with only image-level labeling. The benchmark tests are carried out on the marked regions of 40x magnified whole-slide cytology images and the normal/abnormal label and their corresponding possibilities are predicted. Evaluated on the real-life clinical data, our proposed model shows high accuracy and efficiency by weakly-supervised learning. 1