Stable classification of diabetic structures from incorrectly labeled optical coherence tomography angiography en face images using multi instance learning
Abstract:We present a multiple instance learning-based network, MIL-ResNet14, detecting biomarkers for diabetic retinopathy in a widefield optical coherence tomography angiography dataset with high accuracy, without the necessity of annotations other than the information of whether a scan stems from a diabetic patient or not. Previously introduced deep learning-based classifiers were able to support the detection of diabetic biomarkers in OCTA images, however, require expert labeling on a pixel-level, a labor-intensive… Show more
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