Computer Vision News 26 Deep Learning for the Eyes We’re kicking off the Deep Learning for Ophthalmology interview series with multiple-instance learning. A huge thank you goes to José Morano Sánchez, who introduced me to his recently published work Weakly-Supervised Detection of Amd-related Lesions in Color Fundus Images Using Explainable Deep Learning. The goal of the research was to create a pipeline that is able to diagnose age-related macular degeneration (AMD) which is a common cause of irreversible blindness. Instead of using a simple classification black box model for image-level labels of the nine lesion classes in their AMDLesions dataset, weakly supervised learning is applied in order to generate one activation mask for each lesion type to enhance the explainability. Now, how does this connect to multiple-instance learning? As you probably already know, in multipleinstance learning, we have a bag (image) of instances (pixels or groups of pixels). Multiple labels are provided for the entire bag (imageby Christina Bornberg @datascEYEnce Hello, I am Christina! Welcome to the new RSIP Vision column datascEYEnce! I am interested in deep learning applied to ophthalmology! I just finished my master’s in medical image analysis and am now working at the Singapore Eye Research Institute. featuring José Morano Sánchez José is currently a doctoral research scientist in the Christian Doppler Laboratory for Artificial Intelligence in Retina at the Medical University of Vienna. He received his bachelor’s and master’s degrees from the University of A Coruña in Spain, where he also pursued the research on weakly supervised learning which we are focusing on here today. Multiple-instance Learning Inspired Explainable Deep Learning Network
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