Fine-Grained Multi-Instance Classification in Microscopy Through Deep Attention

Fine-grained object recognition and classification in biomedical images poses a number of challenges. Images typically contain multiple instances (e.g. glands) and the recognition of salient structures is confounded by visually complex backgrounds. Due to the cost of data acquisition or the limited availability of specimens, data sets tend to be small. We propose a simple yet effective attention based deep architecture to address these issues, specially to improve background suppression and recognition of important instances per image. Attention maps per instance are learnt in an end-to-end fashion. Microscopic images of fungi (new data) and a publicly available Breast Cancer Histology benchmark dataset are used to demonstrate the performance of the proposed approach. Experimental results suggest that the proposed approach advances the state-of-the-art.