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Citation: Wang J, Yeh TN, Chakraborty R, Yu SX, Lin MC. A deep learning approach for meibomian gland atrophy evaluation in meibography images. Trans Vis Sci Tech. 2019;8(6):37, https://doi. Purpose:To develop a deep learning approach to digitally segmenting meibomian gland atrophy area and computing percent atrophy in meibography images.Methods: A total of 706 meibography images with corresponding meiboscores were collected and annotated for each one with eyelid and atrophy regions. The dataset was then divided into the development and evaluation sets. The development set was used to train and tune the deep learning model, while the evaluation set was used to evaluate the performance of the model. Results: Four hundred ninety-seven meibography images were used for training and tuning the deep learning model while the remaining 209 images were used for evaluations. The algorithm achieves 95.6% meiboscore grading accuracy on average, largely outperforming the lead clinical investigator (LCI) by 16.0% and the clinical team by 40.6%. Our algorithm also achieves 97.6% and 95.4% accuracy for eyelid and atrophy segmentations, respectively, as well as 95.5% and 66.7% mean intersection over union accuracies (mean IU), respectively. The average root-mean-square deviation (RMSD) of the percent atrophy prediction is 6.7%. Conclusions:The proposed deep learning approach can automatically segment the total eyelid and meibomian gland atrophy regions, as well as compute percent atrophy with high accuracy and consistency. This provides quantitative information of the gland atrophy severity based on meibography images.Translational Relevance: Based on deep neural networks, the study presents an accurate and consistent gland atrophy evaluation method for meibography images, and may contribute to improved understanding of meibomian gland dysfunction.

Transformers have emerged as a powerful tool for a broad range of natural language processing tasks. A key component that drives the impressive performance of Transformers is the self-attention mechanism that encodes the influence or dependence of other tokens on each specific token. While beneficial, the quadratic complexity of self-attention on the input sequence length has limited its application to longer sequences - a topic being actively studied in the community. To address this limitation, we propose Nyströmformer - a model that exhibits favorable scalability as a function of sequence length. Our idea is based on adapting the Nyström method to approximate standard self-attention with O(n) complexity. The scalability of Nyströmformer enables application to longer sequences with thousands of tokens. We perform evaluations on multiple downstream tasks on the GLUE benchmark and IMDB reviews with standard sequence length, and find that our Nyströmformer performs comparably, or in a few cases, even slightly better, than standard self-attention. On longer sequence tasks in the Long Range Arena (LRA) benchmark, Nyströmformer performs favorably relative to other efficient self-attention methods. Our code is available at https://github.com/mlpen/Nystromformer.