@misc{aptos2019_blindness_detection,
  author       = "{APTOS}",
  title        = "{APTOS} 2019 Blindness Detection",
  year         = "2019",
  url          = "https://www.kaggle.com/competitions/aptos2019-blindness-detection/data",
  note         = "[Accessed: 2025-01-22]"
}
@article{caron2021emerging,
  title={Emerging Properties in Self-Supervised Vision Transformers},
  author={Caron, Mathilde and Touvron, Hugo and Misra, Ishan and others},
  journal={arXiv preprint arXiv:2104.14294},
  year={2021}
}
@article{cen_automatic_2021,
  title = {Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks},
  author = {Cen, L.P. and Ji, J. and Lin, J.W. and others},
  journal = {Nature Communications},
  volume = {12},
  pages = {4828},
  year = {2021},
  doi = {10.1038/s41467-021-25138-w},
  url = {https://doi.org/10.1038/s41467-021-25138-w}
}
@ARTICLE{Decenciere2014-xf,
  title     = "Feedback on a publicly distributed image database: The Messidor
               database",
  author    = "Decencière, Etienne and Zhang, Xiwei and Cazuguel, Guy and Lay,
               Bruno and Cochener, Béatrice and Trone, Caroline and Gain,
               Philippe and Ordonez, Richard and Massin, Pascale and Erginay,
               Ali and Charton, Béatrice and Klein, Jean-Claude",
  journal   = "Image Anal. Stereol.",
  publisher = "Slovenian Society for Stereology and Quantitative Image Analysis",
  volume    =  33,
  number    =  3,
  pages     =  231,
  abstract  = "The Messidor database, which contains hundreds of eye fundus
               images, has been publicly distributed since 2008. It was created
               by the Messidor project in order to evaluate automatic lesion
               segmentation and diabetic retinopathy grading methods. Designing,
               producing and maintaining such a database entails significant
               costs. By publicly sharing it, one hopes to bring a valuable
               resource to the public research community. However, the real
               interest and benefit of the research community is not easy to
               quantify. We analyse here the feedback on the Messidor database,
               after more than 6 years of diffusion. This analysis should apply
               to other similar research databases.",
  month     =  aug,
  year      =  2014
}
@ARTICLE{Durston2001-bd,
  title     = "Anatomical {MRI} of the developing human brain: what have we
               learned?",
  author    = "Durston, S and Hulshoff Pol, H E and Casey, B J and Giedd, J N
               and Buitelaar, J K and van Engeland, H",
  journal   = "J. Am. Acad. Child Adolesc. Psychiatry",
  publisher = "Elsevier BV",
  volume    =  40,
  number    =  9,
  pages     = "1012--1020",
  abstract  = "OBJECTIVE: To critically review and integrate the existing
               literature on magnetic resonance imaging (MRI) studies of the
               normally developing brain in childhood and adolescence and
               discuss the implications for clinical MRI studies. METHOD:
               Changes in regional brain volume with age and differences between
               the sexes are summarized from reports in refereed journal
               articles pertaining to MRI of the developing human brain.
               RESULTS: White matter volume increases with age. Gray matter
               volumes increase during childhood and then decrease before
               adulthood. On average, boys have larger brains than girls; after
               correction for overall brain volume the caudate is relatively
               larger in girls, and the amygdala is relatively larger in boys.
               Differences are of clinical interest given gender-related
               differences in the age of onset, symptomatology, and prevalence
               noted for nearly all childhood-onset psychiatric disorders.
               Attention-deficit/hyperactivity disorder is frequently used as an
               example to demonstrate these points. CONCLUSIONS: Understanding
               the developmental trajectories of normal brain development and
               differences between the sexes is important for the interpretation
               of clinical imaging studies.",
  month     =  sep,
  year      =  2001,
  language  = "en"
}
@ARTICLE{Goceri2023-zh,
  title     = "Medical image data augmentation: techniques, comparisons and
               interpretations",
  author    = "Goceri, Evgin",
  journal   = "Artif. Intell. Rev.",
  publisher = "Springer Science and Business Media LLC",
  pages     = "1--45",
  abstract  = "Designing deep learning based methods with medical images has
               always been an attractive area of research to assist clinicians
               in rapid examination and accurate diagnosis. Those methods need a
               large number of datasets including all variations in their
               training stages. On the other hand, medical images are always
               scarce due to several reasons, such as not enough patients for
               some diseases, patients do not want to allow their images to be
               used, lack of medical equipment or equipment, inability to obtain
               images that meet the desired criteria. This issue leads to bias
               in datasets, overfitting, and inaccurate results. Data
               augmentation is a common solution to overcome this issue and
               various augmentation techniques have been applied to different
               types of images in the literature. However, it is not clear which
               data augmentation technique provides more efficient results for
               which image type since different diseases are handled, different
               network architectures are used, and these architectures are
               trained and tested with different numbers of data sets in the
               literature. Therefore, in this work, the augmentation techniques
               used to improve performances of deep learning based diagnosis of
               the diseases in different organs (brain, lung, breast, and eye)
               from different imaging modalities (MR, CT, mammography, and
               fundoscopy) have been examined. Also, the most commonly used
               augmentation methods have been implemented, and their
               effectiveness in classifications with a deep network has been
               discussed based on quantitative performance evaluations.
               Experiments indicated that augmentation techniques should be
               chosen carefully according to image types.",
  month     =  mar,
  year      =  2023,
  keywords  = "Data augmentation; GAN; Medical images; Synthesis",
  language  = "en"
}
@ARTICLE{He2021-cc,
  title         = "Masked Autoencoders Are Scalable Vision Learners",
  author        = "He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao
                   and Dollár, Piotr and Girshick, Ross",
  journal       = "arXiv [cs.CV]",
  abstract      = "This paper shows that masked autoencoders (MAE) are scalable
                   self-supervised learners for computer vision. Our MAE
                   approach is simple: we mask random patches of the input image
                   and reconstruct the missing pixels. It is based on two core
                   designs. First, we develop an asymmetric encoder-decoder
                   architecture, with an encoder that operates only on the
                   visible subset of patches (without mask tokens), along with a
                   lightweight decoder that reconstructs the original image from
                   the latent representation and mask tokens. Second, we find
                   that masking a high proportion of the input image, e.g.,
                   75\%, yields a nontrivial and meaningful self-supervisory
                   task. Coupling these two designs enables us to train large
                   models efficiently and effectively: we accelerate training
                   (by 3x or more) and improve accuracy. Our scalable approach
                   allows for learning high-capacity models that generalize
                   well: e.g., a vanilla ViT-Huge model achieves the best
                   accuracy (87.8\%) among methods that use only ImageNet-1K
                   data. Transfer performance in downstream tasks outperforms
                   supervised pre-training and shows promising scaling behavior.",
  month         =  nov,
  year          =  2021,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CV"
}
@ARTICLE{Hendrycks2019-yf,
  title     = "Using self-supervised learning can improve model robustness and
               uncertainty",
  author    = "Hendrycks, Dan and Mazeika, Mantas and Kadavath, Saurav and Song,
               D",
  journal   = "Neural Inf Process Syst",
  publisher = "proceedings.neurips.cc",
  volume    = "abs/1906.12340",
  abstract  = "Self-supervision provides effective representations for
               downstream tasks without requiring labels. However, existing
               approaches lag behind fully supervised training and are often not
               thought beneficial beyond obviating the need for annotations. We
               find that self-supervision can benefit robustness in a variety of
               ways, including robustness to adversarial examples, label
               corruption, and common input corruptions. Additionally,
               self-supervision greatly benefits out-of-distribution detection
               on difficult, near-distribution outliers, so much so that it
               exceeds the performance of fully supervised methods. These
               results demonstrate the promise of self-supervision for improving
               robustness and uncertainty estimation and establish these tasks
               as new axes of evaluation for future self-supervised learning
               research.",
  month     =  jun,
  year      =  2019
}
@ARTICLE{Islam2022-ri,
  title     = "Applying supervised contrastive learning for the detection of
               diabetic retinopathy and its severity levels from fundus images",
  author    = "Islam, Md Robiul and Abdulrazak, Lway Faisal and Nahiduzzaman, Md
               and Goni, Md Omaer Faruq and Anower, Md Shamim and Ahsan, Mominul
               and Haider, Julfikar and Kowalski, Marcin",
  journal   = "Comput. Biol. Med.",
  publisher = "Elsevier BV",
  volume    =  146,
  number    =  105602,
  pages     =  105602,
  abstract  = "Diabetic Retinopathy (DR) is a major complication in human eyes
               among the diabetic patients. Early detection of the DR can save
               many patients from permanent blindness. Various artificial
               intelligent based systems have been proposed and they outperform
               human analysis in accurate detection of the DR. In most of the
               traditional deep learning models, the cross-entropy is used as a
               common loss function in a single stage end-to-end training
               method. However, it has been recently identified that this loss
               function has some limitations such as poor margin leading to
               false results, sensitive to noisy data and hyperparameter
               variations. To overcome these issues, supervised contrastive
               learning (SCL) has been introduced. In this study, SCL method, a
               two-stage training method with supervised contrastive loss
               function was proposed for the first time to the best of authors'
               knowledge to identify the DR and its severity stages from fundus
               images (FIs) using ``APTOS 2019 Blindness Detection'' dataset.
               ``Messidor-2'' dataset was also used to conduct experiments for
               further validating the model's performance. Contrast Limited
               Adaptive Histogram Equalization (CLAHE) was applied for enhancing
               the image quality and the pre-trained Xception CNN model was
               deployed as the encoder with transfer learning. To interpret the
               SCL of the model, t-SNE method was used to visualize the
               embedding space (unit hyper sphere) composed of 128 D space into
               a 2 D space. The proposed model achieved a test accuracy of
               98.36\%, and AUC score of 98.50\% to identify the DR (Binary
               classification) and a test accuracy of 84.364\%, and AUC score of
               93.819\% for five stages grading with the APTOS 2019 dataset.
               Other evaluation metrics (precision, recall, F1-score) were also
               determined with APTOS 2019 as well as with Messidor-2 for
               analyzing the performance of the proposed model. It was also
               concluded that the proposed method achieved better performance in
               detecting the DR compared to the conventional CNN without SCL and
               other state-of-the-art methods.",
  month     =  jul,
  year      =  2022,
  keywords  = "CLAHE; CNN; Diabetic retinopathy; Image analysis; Supervised
               contrastive learning; t-SNE",
  language  = "en"
}
@ARTICLE{Jaiswal2020ve,
  title     = "A survey on contrastive self-supervised learning",
  author    = "Jaiswal, Ashish and Babu, Ashwin Ramesh and Zadeh, Mohammad Zaki
               and Banerjee, Debapriya and Makedon, F",
  journal   = "Technologies (Basel)",
  publisher = "mdpi.com",
  volume    = "abs/2011.00362",
  abstract  = "Self-supervised learning has gained popularity because of its
               ability to avoid the cost of annotating large-scale datasets. It
               is capable of adopting self-defined pseudolabels as supervision
               and use the learned representations for several downstream tasks.
               Specifically, contrastive learning has recently become a dominant
               component in self-supervised learning for computer vision,
               natural language processing (NLP), and other domains. It aims at
               embedding augmented versions of the same sample close to each
               other while trying to push away embeddings from different
               samples. This paper provides an extensive review of
               self-supervised methods that follow the contrastive approach. The
               work explains commonly used pretext tasks in a contrastive
               learning setup, followed by different architectures that have
               been proposed so far. Next, we present a performance comparison
               of different methods for multiple downstream tasks such as image
               classification, object detection, and action recognition.
               Finally, we conclude with the limitations of the current methods
               and the need for further techniques and future directions to make
               meaningful progress.",
  month     =  oct,
  year      =  2020
}
@misc{jr2ngb_cataractdataset,
  author       = {jr2ngb},
  title        = {Cataract Dataset},
  year         = {2023},
  url          = {https://www.kaggle.com/datasets/jr2ngb/cataractdataset/data},
  note         = {[Accessed: 2025-01-22]}
}
@ARTICLE{Kang2022-li,
  title         = "Benchmarking self-supervised learning on diverse pathology
                   datasets",
  author        = "Kang, Mingu and Song, Heon and Park, Seonwook and Yoo,
                   Donggeun and Pereira, Sérgio",
  journal       = "arXiv [cs.CV]",
  abstract      = "Computational pathology can lead to saving human lives, but
                   models are annotation hungry and pathology images are
                   notoriously expensive to annotate. Self-supervised learning
                   has shown to be an effective method for utilizing unlabeled
                   data, and its application to pathology could greatly benefit
                   its downstream tasks. Yet, there are no principled studies
                   that compare SSL methods and discuss how to adapt them for
                   pathology. To address this need, we execute the largest-scale
                   study of SSL pre-training on pathology image data, to date.
                   Our study is conducted using 4 representative SSL methods on
                   diverse downstream tasks. We establish that large-scale
                   domain-aligned pre-training in pathology consistently
                   out-performs ImageNet pre-training in standard SSL settings
                   such as linear and fine-tuning evaluations, as well as in
                   low-label regimes. Moreover, we propose a set of
                   domain-specific techniques that we experimentally show leads
                   to a performance boost. Lastly, for the first time, we apply
                   SSL to the challenging task of nuclei instance segmentation
                   and show large and consistent performance improvements under
                   diverse settings.",
  month         =  dec,
  year          =  2022,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CV"
}
@article{kovalyk_papila_2022,
  title = {PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment},
  author = {Kovalyk, O. and Morales-Sánchez, J. and Verdú-Monedero, R. and others},
  journal = {Scientific Data},
  volume = {9},
  number = {291},
  year = {2022},
  doi = {10.1038/s41597-022-01388-1},
  url = {https://doi.org/10.1038/s41597-022-01388-1}
}
@ARTICLE{LeKhac2020zn,
  title     = "Contrastive Representation Learning: A framework and review",
  author    = "L\^{e} Kh{\'a}c, Ph{\'u}c H and Healy, G and Smeaton, A",
  journal   = "IEEE Access",
  publisher = "ieeexplore.ieee.org",
  volume    =  8,
  pages     = "193907--193934",
  abstract  = "Contrastive Learning has recently received interest due to its
               success in self-supervised representation learning in the
               computer vision domain. However, the origins of Contrastive
               Learning date as far back as the 1990s and its development has
               spanned across many fields and domains including Metric Learning
               and natural language processing. In this paper, we provide a
               comprehensive literature review and we propose a general
               Contrastive Representation Learning framework that simplifies and
               unifies many different contrastive learning methods. We also
               provide a taxonomy for each of the components of contrastive
               learning in order to summarise it and distinguish it from other
               forms of machine learning. We then discuss the inductive biases
               which are present in any contrastive learning system and we
               analyse our framework under different views from various
               sub-fields of Machine Learning. Examples of how contrastive
               learning has been applied in computer vision, natural language
               processing, audio processing, and others, as well as in
               Reinforcement Learning are also presented. Finally, we discuss
               the challenges and some of the most promising future research
               directions ahead.",
  month     =  oct,
  year      =  2020
}
@article{legras2018distribution,
  title={Distribution of cone density, spacing and arrangement in adult healthy retinas with adaptive optics flood illumination},
  author={Legras, Richard and Gaudric, Alain and Woog, Kelly},
  journal={PloS one},
  volume={13},
  number={1},
  pages={e0191141},
  year={2018},
  publisher={Public Library of Science},
  doi={10.1371/journal.pone.0191141},
  pmid={29338027},
  pmcid={PMC5770065}
}
@ARTICLE{Liu2020-dy,
  title         = "Self-supervised learning: Generative or contrastive",
  author        = "Liu, Xiao and Zhang, Fanjin and Hou, Zhenyu and Wang, Zhaoyu
                   and Mian, Li and Zhang, Jing and Tang, Jie",
  journal       = "arXiv [cs.LG]",
  abstract      = "Deep supervised learning has achieved great success in the
                   last decade. However, its deficiencies of dependence on
                   manual labels and vulnerability to attacks have driven people
                   to explore a better solution. As an alternative,
                   self-supervised learning attracts many researchers for its
                   soaring performance on representation learning in the last
                   several years. Self-supervised representation learning
                   leverages input data itself as supervision and benefits
                   almost all types of downstream tasks. In this survey, we take
                   a look into new self-supervised learning methods for
                   representation in computer vision, natural language
                   processing, and graph learning. We comprehensively review the
                   existing empirical methods and summarize them into three main
                   categories according to their objectives: generative,
                   contrastive, and generative-contrastive (adversarial). We
                   further investigate related theoretical analysis work to
                   provide deeper thoughts on how self-supervised learning
                   works. Finally, we briefly discuss open problems and future
                   directions for self-supervised learning. An outline slide for
                   the survey is provided.",
  month         =  jun,
  year          =  2020,
  archivePrefix = "arXiv",
  primaryClass  = "cs.LG"
}
@INCOLLECTION{Long2024-aq,
  title     = "A classification method for diabetic retinopathy based on
               self-supervised learning",
  author    = "Long, Fei and Xiong, Haoren and Sang, Jun",
  booktitle = "Advanced Intelligent Computing in Bioinformatics",
  publisher = "Springer Nature Singapore",
  address   = "Singapore",
  pages     = "347--357",
  series    = "Lecture notes in computer science",
  year      =  2024,
  language  = "en"
}
@ARTICLE{Maaten2008-vj,
  title    = "Visualizing Data using t-{SNE}",
  author   = "Maaten, L and Hinton, Geoffrey E",
  journal  = "Journal of Machine Learning Research",
  volume   =  9,
  number   =  86,
  pages    = "2579--2605",
  abstract = "We present a new technique called “t-SNE” that visualizes
              high-dimensional data by giving each datapoint a location in a two
              or three-dimensional map. The technique is a variation of
              Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is
              much easier to optimize, and produces significantly better
              visualizations by reducing the tendency to crowd points together
              in the center of the map. t-SNE is better than existing techniques
              at creating a single map that reveals structure at many different
              scales. This is particularly important for high-dimensional data
              that lie on several different, but related, low-dimensional
              manifolds, such as images of objects from multiple classes seen
              from multiple viewpoints. For visualizing the structure of very
              large datasets, we show how t-SNE can use random walks on
              neighborhood graphs to allow the implicit structure of all of the
              data to influence the way in which a subset of the data is
              displayed. We illustrate the performance of t-SNE on a wide
              variety of datasets and compare it with many other non-parametric
              visualization techniques, including Sammon mapping, Isomap, and
              Locally Linear Embedding. The visualizations produced by t-SNE are
              significantly better than those produced by the other techniques
              on almost all of the datasets.",
  year     =  2008
}
@ARTICLE{Misra2019-kz,
  title     = "Self-supervised learning of pretext-invariant representations",
  author    = "Misra, Ishan and Maaten, L",
  journal   = "Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit.",
  publisher = "openaccess.thecvf.com",
  pages     = "6706--6716",
  abstract  = "The goal of self-supervised learning from images is to construct
               image representations that are semantically meaningful via
               pretext tasks that do not require semantic annotations. Many
               pretext tasks lead to representations that are covariant with
               image transformations. We argue that, instead, semantic
               representations ought to be invariant under such transformations.
               Specifically, we develop Pretext-Invariant Representation
               Learning (PIRL, pronounced as `pearl') that learns invariant
               representations based on pretext tasks. We use PIRL with a
               commonly used pretext task that involves solving jigsaw puzzles.
               We find that PIRL substantially improves the semantic quality of
               the learned image representations. Our approach sets a new
               state-of-the-art in self-supervised learning from images on
               several popular benchmarks for self-supervised learning. Despite
               being unsupervised, PIRL outperforms supervised pre-training in
               learning image representations for object detection. Altogether,
               our results demonstrate the potential of self-supervised
               representations with good invariance properties.",
  month     =  dec,
  year      =  2019
}
@misc{oquab2024dinov2learningrobustvisual,
      title={DINOv2: Learning Robust Visual Features without Supervision}, 
      author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski},
      year={2024},
      eprint={2304.07193},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2304.07193}, 
}
@misc{PATTON200699,
  title = {Retinal Image Analysis: {{Concepts}}, Applications and Potential},
  author = {Patton, Niall and Aslam, Tariq M. and MacGillivray, Thomas and Deary, Ian J. and Dhillon, Baljean and Eikelboom, Robert H. and Yogesan, Kanagasingam and Constable, Ian J.},
  year = {2006},
  journal = {Progress in Retinal and Eye Research},
  volume = {25},
  number = {1},
  pages = {99--127},
  issn = {1350-9462},
  doi = {10.1016/j.preteyeres.2005.07.001},
  abstract = {As digital imaging and computing power increasingly develop, so too does the potential to use these technologies in ophthalmology. Image processing, analysis and computer vision techniques are increasing in prominence in all fields of medical science, and are especially pertinent to modern ophthalmology, as it is heavily dependent on visually oriented signs. The retinal microvasculature is unique in that it is the only part of the human circulation that can be directly visualised non-invasively in vivo, readily photographed and subject to digital image analysis. Exciting developments in image processing relevant to ophthalmology over the past 15 years includes the progress being made towards developing automated diagnostic systems for conditions, such as diabetic retinopathy, age-related macular degeneration and retinopathy of prematurity. These diagnostic systems offer the potential to be used in large-scale screening programs, with the potential for significant resource savings, as well as being free from observer bias and fatigue. In addition, quantitative measurements of retinal vascular topography using digital image analysis from retinal photography have been used as research tools to better understand the relationship between the retinal microvasculature and cardiovascular disease. Furthermore, advances in electronic media transmission increase the relevance of using image processing in `teleophthalmology' as an aid in clinical decision-making, with particular relevance to large rural-based communities. In this review, we outline the principles upon which retinal digital image analysis is based. We discuss current techniques used to automatically detect landmark features of the fundus, such as the optic disc, fovea and blood vessels. We review the use of image analysis in the automated diagnosis of pathology (with particular reference to diabetic retinopathy). We also review its role in defining and performing quantitative measurements of vascular topography, how these entities are based on `optimisation' principles and how they have helped to describe the relationship between systemic cardiovascular disease and retinal vascular changes. We also review the potential future use of fundal image analysis in telemedicine.}
}
@ARTICLE{Porwal2018-pd,
  title     = "Indian Diabetic Retinopathy Image Dataset ({IDRiD}): A database
               for diabetic retinopathy screening research",
  author    = "Porwal, Prasanna and Pachade, Samiksha and Kamble, Ravi and
               Kokare, Manesh and Deshmukh, Girish and Sahasrabuddhe, Vivek and
               Meriaudeau, Fabrice",
  journal   = "Data (Basel)",
  publisher = "MDPI AG",
  volume    =  3,
  number    =  3,
  pages     =  25,
  abstract  = "Diabetic Retinopathy is the most prevalent cause of avoidable
               vision impairment, mainly affecting the working-age population in
               the world. Recent research has given a better understanding of
               the requirement in clinical eye care practice to identify better
               and cheaper ways of identification, management, diagnosis and
               treatment of retinal disease. The importance of diabetic
               retinopathy screening programs and difficulty in achieving
               reliable early diagnosis of diabetic retinopathy at a reasonable
               cost needs attention to develop computer-aided diagnosis tool.
               Computer-aided disease diagnosis in retinal image analysis could
               ease mass screening of populations with diabetes mellitus and
               help clinicians in utilizing their time more efficiently. The
               recent technological advances in computing power, communication
               systems, and machine learning techniques provide opportunities to
               the biomedical engineers and computer scientists to meet the
               requirements of clinical practice. Diverse and representative
               retinal image sets are essential for developing and testing
               digital screening programs and the automated algorithms at their
               core. To the best of our knowledge, IDRiD (Indian Diabetic
               Retinopathy Image Dataset), is the first database representative
               of an Indian population. It constitutes typical diabetic
               retinopathy lesions and normal retinal structures annotated at a
               pixel level. The dataset provides information on the disease
               severity of diabetic retinopathy, and diabetic macular edema for
               each image. This makes it perfect for development and evaluation
               of image analysis algorithms for early detection of diabetic
               retinopathy.",
  month     =  jul,
  year      =  2018,
  language  = "en"
}
@ARTICLE{Perez-Garcia2021-am,
  title     = "{TorchIO}: A Python library for efficient loading, preprocessing,
               augmentation and patch-based sampling of medical images in deep
               learning",
  author    = "Pérez-García, Fernando and Sparks, Rachel and Ourselin, Sébastien",
  journal   = "Comput. Methods Programs Biomed.",
  publisher = "Elsevier BV",
  volume    =  208,
  number    =  106236,
  pages     =  106236,
  abstract  = "BACKGROUND AND OBJECTIVE: Processing of medical images such as
               MRI or CT presents different challenges compared to RGB images
               typically used in computer vision. These include a lack of labels
               for large datasets, high computational costs, and the need of
               metadata to describe the physical properties of voxels. Data
               augmentation is used to artificially increase the size of the
               training datasets. Training with image subvolumes or patches
               decreases the need for computational power. Spatial metadata
               needs to be carefully taken into account in order to ensure a
               correct alignment and orientation of volumes. METHODS: We present
               TorchIO, an open-source Python library to enable efficient
               loading, preprocessing, augmentation and patch-based sampling of
               medical images for deep learning. TorchIO follows the style of
               PyTorch and integrates standard medical image processing
               libraries to efficiently process images during training of neural
               networks. TorchIO transforms can be easily composed, reproduced,
               traced and extended. Most transforms can be inverted, making the
               library suitable for test-time augmentation and estimation of
               aleatoric uncertainty in the context of segmentation. We provide
               multiple generic preprocessing and augmentation operations as
               well as simulation of MRI-specific artifacts. RESULTS: Source
               code, comprehensive tutorials and extensive documentation for
               TorchIO can be found at http://torchio.rtfd.io/. The package can
               be installed from the Python Package Index (PyPI) running pip
               install torchio. It includes a command-line interface which
               allows users to apply transforms to image files without using
               Python. Additionally, we provide a graphical user interface
               within a TorchIO extension in 3D Slicer to visualize the effects
               of transforms. CONCLUSION: TorchIO was developed to help
               researchers standardize medical image processing pipelines and
               allow them to focus on the deep learning experiments. It
               encourages good open-science practices, as it supports experiment
               reproducibility and is version-controlled so that the software
               can be cited precisely. Due to its modularity, the library is
               compatible with other frameworks for deep learning with medical
               images.",
  month     =  sep,
  year      =  2021,
  keywords  = "Data augmentation; Deep learning; Medical image computing;
               Preprocessing",
  language  = "en"
}
@INPROCEEDINGS{Shahapure2020-sd,
  title     = "Cluster quality analysis using silhouette score",
  author    = "Shahapure, Ketan Rajshekhar and Nicholas, Charles",
  booktitle = "2020 IEEE 7th International Conference on Data Science and
               Advanced Analytics (DSAA)",
  publisher = "IEEE",
  pages     = "747--748",
  abstract  = "Clustering is an important phase in data mining. Selecting the
               number of clusters in a clustering algorithm, e.g. choosing the
               best value of k in the various k-means algorithms [1], can be
               difficult. We studied the use of silhouette scores and scatter
               plots to suggest, and then validate, the number of clusters we
               specified in running the k-means clustering algorithm on two
               publicly available data sets. Scikit-learn's [4] silhouette score
               method, which is a measure of the quality of a cluster, was used
               to find the mean silhouette co-efficient of all the samples for
               different number of clusters. The highest silhouette score
               indicates the optimal number of clusters. We present several
               instances of utilizing the silhouette score to determine the best
               value of k for those data sets.",
  month     =  oct,
  year      =  2020
}
@InProceedings{Singh_2021_CVPR,
    author    = {Singh, Ankit and Chakraborty, Omprakash and Varshney, Ashutosh and Panda, Rameswar and Feris, Rogerio and Saenko, Kate and Das, Abir},
    title     = {Semi-Supervised Action Recognition With Temporal Contrastive Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {10389-10399}
}
@ARTICLE{Van_der_Sluijs2023-xc,
  title         = "Exploring image augmentations for Siamese representation
                   learning with chest {X}-Rays",
  author        = "van der Sluijs, Rogier and Bhaskhar, Nandita and Rubin,
                   Daniel and Langlotz, Curtis and Chaudhari, Akshay",
  journal       = "arXiv [eess.IV]",
  abstract      = "Image augmentations are quintessential for effective visual
                   representation learning across self-supervised learning
                   techniques. While augmentation strategies for natural imaging
                   have been studied extensively, medical images are vastly
                   different from their natural counterparts. Thus, it is
                   unknown whether common augmentation strategies employed in
                   Siamese representation learning generalize to medical images
                   and to what extent. To address this challenge, in this study,
                   we systematically assess the effect of various augmentations
                   on the quality and robustness of the learned representations.
                   We train and evaluate Siamese Networks for abnormality
                   detection on chest X-Rays across three large datasets
                   (MIMIC-CXR, CheXpert and VinDR-CXR). We investigate the
                   efficacy of the learned representations through experiments
                   involving linear probing, fine-tuning, zero-shot transfer,
                   and data efficiency. Finally, we identify a set of
                   augmentations that yield robust representations that
                   generalize well to both out-of-distribution data and
                   diseases, while outperforming supervised baselines using just
                   zero-shot transfer and linear probes by up to 20\%. Our code
                   is available at https://github.com/StanfordMIMI/siaug.",
  month         =  jan,
  year          =  2023,
  archivePrefix = "arXiv",
  primaryClass  = "eess.IV"
}
@article{wagner2022alzeye,
  title={AlzEye: longitudinal record-level linkage of ophthalmic imaging and hospital admissions of 353 157 patients in London, UK},
  author={Wagner, Siegfried Karl and Hughes, Fintan and Cortina-Borja, Mario and Pontikos, Nikolas and Struyven, Robbert and Liu, Xiaoxuan and Montgomery, Hugh and Alexander, Daniel C and Topol, Eric and Petersen, Steffen Erhard and others},
  journal={BMJ open},
  volume={12},
  number={3},
  pages={e058552},
  year={2022},
  publisher={British Medical Journal Publishing Group}
}
@article{WEN2021103145,
title = {Rethinking pre-training on medical imaging},
journal = {Journal of Visual Communication and Image Representation},
volume = {78},
pages = {103145},
year = {2021},
issn = {1047-3203},
doi = {https://doi.org/10.1016/j.jvcir.2021.103145},
url = {https://www.sciencedirect.com/science/article/pii/S1047320321000894},
author = {Yang Wen and Leiting Chen and Yu Deng and Chuan Zhou},
keywords = {Transfer learning, Medical image analysis, Convolutional neural network, Survival prediction},
abstract = {Transfer learning from natural image datasets, such as ImageNet, is common for applying deep learning to medical imaging. However, the modalities of natural and medical images differ considerably, and the reason for the latest medical research preferring ImageNet to medical data is questionable. In this study, we investigated the properties of medical pre-training and its transfer effectiveness on various medical tasks. Through an intuitive convolution-based analysis, we determined the modality characteristics of images. Surprisingly, medical pre-training showed exceptional performance for a classification task but not for a segmentation task since medical data are visually homogeneous and lack morphological information. Using data with diverse modalities helped overcome such drawbacks, resulting in medical pre-training achieving performance comparable to pre-training with ImageNet with considerably fewer samples than ImageNet for both aforementioned tasks. Finally, a study of learned representations and realistic scenarios indicated that while ImageNet is the best choice for medical imaging, medical pre-training has significant potential.}
}
@ARTICLE{Xie2021-aj,
  title         = "{DetCo}: Unsupervised contrastive learning for object
                   detection",
  author        = "Xie, Enze and Ding, Jian and Wang, Wenhai and Zhan, Xiaohang
                   and Xu, Hang and Sun, Peize and Li, Zhenguo and Luo, Ping",
  journal       = "arXiv [cs.CV]",
  abstract      = "Unsupervised contrastive learning achieves great success in
                   learning image representations with CNN. Unlike most recent
                   methods that focused on improving accuracy of image
                   classification, we present a novel contrastive learning
                   approach, named DetCo, which fully explores the contrasts
                   between global image and local image patches to learn
                   discriminative representations for object detection. DetCo
                   has several appealing benefits. (1) It is carefully designed
                   by investigating the weaknesses of current self-supervised
                   methods, which discard important representations for object
                   detection. (2) DetCo builds hierarchical intermediate
                   contrastive losses between global image and local patches to
                   improve object detection, while maintaining global
                   representations for image recognition. Theoretical analysis
                   shows that the local patches actually remove the contextual
                   information of an image, improving the lower bound of mutual
                   information for better contrastive learning. (3) Extensive
                   experiments on PASCAL VOC, COCO and Cityscapes demonstrate
                   that DetCo not only outperforms state-of-the-art methods on
                   object detection, but also on segmentation, pose estimation,
                   and 3D shape prediction, while it is still competitive on
                   image classification. For example, on PASCAL VOC, DetCo-100ep
                   achieves 57.4 mAP, which is on par with the result of
                   MoCov2-800ep. Moreover, DetCo consistently outperforms
                   supervised method by 1.6/1.2/1.0 AP on Mask
                   RCNN-C4/FPN/RetinaNet with 1x schedule. Code will be released
                   at \href{https://github.com/xieenze/DetCo}{\color{blue}{\tt
                   github.com/xieenze/DetCo}}.",
  month         =  feb,
  year          =  2021,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CV"
}
@ARTICLE{Zeng2020-ey,
  title         = "Contrastive self-supervised learning for graph classification",
  author        = "Zeng, Jiaqi and Xie, Pengtao",
  journal       = "arXiv [cs.LG]",
  abstract      = "Graph classification is a widely studied problem and has
                   broad applications. In many real-world problems, the number
                   of labeled graphs available for training classification
                   models is limited, which renders these models prone to
                   overfitting. To address this problem, we propose two
                   approaches based on contrastive self-supervised learning
                   (CSSL) to alleviate overfitting. In the first approach, we
                   use CSSL to pretrain graph encoders on widely-available
                   unlabeled graphs without relying on human-provided labels,
                   then finetune the pretrained encoders on labeled graphs. In
                   the second approach, we develop a regularizer based on CSSL,
                   and solve the supervised classification task and the
                   unsupervised CSSL task simultaneously. To perform CSSL on
                   graphs, given a collection of original graphs, we perform
                   data augmentation to create augmented graphs out of the
                   original graphs. An augmented graph is created by
                   consecutively applying a sequence of graph alteration
                   operations. A contrastive loss is defined to learn graph
                   encoders by judging whether two augmented graphs are from the
                   same original graph. Experiments on various graph
                   classification datasets demonstrate the effectiveness of our
                   proposed methods.",
  month         =  sep,
  year          =  2020,
  archivePrefix = "arXiv",
  primaryClass  = "cs.LG"
}
@article{zhou2021review,
  title={A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises},
  author={Zhou, S Kevin and Greenspan, Hayit and Davatzikos, Christos and Duncan, James S and van Ginneken, Bram and Madabhushi, Anant and Prince, Jerry L and Rueckert, Daniel and Summers, Ronald M},
  journal={Proceedings of the IEEE},
  volume={109},
  number={5},
  pages={820--838},
  year={2021},
  publisher={IEEE},
  doi={10.1109/JPROC.2021.3054390},
  pmid={37786449},
  pmcid={PMC10544772}
}
@article{zhou2022automorph,
  title={AutoMorph: automated retinal vascular morphology quantification via a deep learning pipeline},
  author={Zhou, Yukun and Wagner, Siegfried K and Chia, Mark A and Zhao, An and Xu, Moucheng and Struyven, Robbert and Alexander, Daniel C and Keane, Pearse A and others},
  journal={Translational vision science \& technology},
  volume={11},
  number={7},
  pages={12--12},
  year={2022},
  publisher={The Association for Research in Vision and Ophthalmology}
}
@ARTICLE{Zhou2023-rr,
  title     = "A foundation model for generalizable disease detection from
               retinal images",
  author    = "Zhou, Yukun and Chia, Mark A and Wagner, Siegfried K and Ayhan,
               Murat S and Williamson, Dominic J and Struyven, Robbert R and
               Liu, Timing and Xu, Moucheng and Lozano, Mateo G and
               Woodward-Court, Peter and Kihara, Yuka and {UK Biobank Eye \&
               Vision Consortium} and Altmann, Andre and Lee, Aaron Y and Topol,
               Eric J and Denniston, Alastair K and Alexander, Daniel C and
               Keane, Pearse A",
  journal   = "Nature",
  publisher = "Nature Publishing Group",
  volume    =  622,
  number    =  7981,
  pages     = "156--163",
  abstract  = "Medical artificial intelligence (AI) offers great potential for
               recognizing signs of health conditions in retinal images and
               expediting the diagnosis of eye diseases and systemic disorders1.
               However, the development of AI models requires substantial
               annotation and models are usually task-specific with limited
               generalizability to different clinical applications2. Here, we
               present RETFound, a foundation model for retinal images that
               learns generalizable representations from unlabelled retinal
               images and provides a basis for label-efficient model adaptation
               in several applications. Specifically, RETFound is trained on 1.6
               million unlabelled retinal images by means of self-supervised
               learning and then adapted to disease detection tasks with
               explicit labels. We show that adapted RETFound consistently
               outperforms several comparison models in the diagnosis and
               prognosis of sight-threatening eye diseases, as well as incident
               prediction of complex systemic disorders such as heart failure
               and myocardial infarction with fewer labelled data. RETFound
               provides a generalizable solution to improve model performance
               and alleviate the annotation workload of experts to enable broad
               clinical AI applications from retinal imaging.",
  month     =  oct,
  year      =  2023,
  language  = "en"
}
@ARTICLE{Caron2021-th,
  title         = "Emerging properties in self-supervised vision transformers",
  author        = "Caron, Mathilde and Touvron, Hugo and Misra, Ishan and Jégou,
                   Hervé and Mairal, Julien and Bojanowski, Piotr and Joulin,
                   Armand",
  journal       = "arXiv [cs.CV]",
  abstract      = "In this paper, we question if self-supervised learning
                   provides new properties to Vision Transformer (ViT) that
                   stand out compared to convolutional networks (convnets).
                   Beyond the fact that adapting self-supervised methods to this
                   architecture works particularly well, we make the following
                   observations: first, self-supervised ViT features contain
                   explicit information about the semantic segmentation of an
                   image, which does not emerge as clearly with supervised ViTs,
                   nor with convnets. Second, these features are also excellent
                   k-NN classifiers, reaching 78.3\% top-1 on ImageNet with a
                   small ViT. Our study also underlines the importance of
                   momentum encoder, multi-crop training, and the use of small
                   patches with ViTs. We implement our findings into a simple
                   self-supervised method, called DINO, which we interpret as a
                   form of self-distillation with no labels. We show the synergy
                   between DINO and ViTs by achieving 80.1\% top-1 on ImageNet
                   in linear evaluation with ViT-Base.",
  month         =  apr,
  year          =  2021,
  archivePrefix = "arXiv",
  primaryClass  = "cs.CV"
}