Baskentmuhendislik

Tag: image

  • Apple slices its AI image synthesis times in half with new Stable Diffusion fix

    Apple slices its AI image synthesis times in half with new Stable Diffusion fix

    Two examples of Stable Diffusion-generated artwork provided by Apple.
    Enlarge / Two examples of Stable Diffusion-generated artwork provided by Apple.

    Apple

    On Wednesday, Apple released optimizations that allow the Stable Diffusion AI image generator to run on Apple Silicon using Core ML, Apple’s proprietary framework for machine-learning models. The optimizations will allow app developers to use Apple Neural Engine hardware to run Stable Diffusion about twice as fast as previous Mac-based methods.

    Stable Diffusion (SD), which launched in August, is an open source AI image synthesis model that generates novel images using text input. For example, typing “astronaut on a dragon” into SD will typically create an image of exactly that.

    By releasing the new SD optimizations—available as conversion scripts on GitHub—Apple wants to unlock the full potential of image synthesis on its devices. And it notes this on the Apple Research announcement page: “With the growing number of applications of Stable Diffusion, ensuring that developers can leverage this technology effectively is important for creating apps that creatives everywhere will be able to use.”

    Apple also mentions privacy and avoiding cloud computing costs as advantages to running an AI generation model locally on a Mac or Apple device.

    “The privacy of the end user is protected because any data the user provided as input to the model stays on the user’s device,” says Apple. “Second, after initial download, users don’t require an internet connection to use the model. Finally, locally deploying this model enables developers to reduce or eliminate their server-related costs.”

    Currently, Stable Diffusion generates images fastest on high-end GPUs from Nvidia when run locally on a Windows or Linux PC. For example, generating a 512×512 image at 50 steps on an RTX 3060 takes about 8.7 seconds on our machine.

    By comparison, the conventional method of running Stable Diffusion on an Apple Silicon Mac is far slower, taking about 69.8 seconds to generate a 512×512 image at 50 steps using Diffusion Bee in our tests on an M1 Mac Mini.

    According to Apple’s benchmarks on GitHub, Apple’s new Core ML SD optimizations can generate a 512×512 50-step image on an M1 chip in 35 seconds. An M2 does the task in 23 seconds, and Apple’s most powerful Silicon chip, the M1 Ultra, can achieve the same result in only nine seconds. That’s a dramatic improvement, cutting generation time almost in half in the case of the M1.

    Apple’s GitHub release is a Python package that converts Stable Diffusion models from PyTorch to Core ML and includes a Swift package for model deployment. The optimizations work for Stable Diffusion 1.4, 1.5, and the newly released 2.0.

    At the moment, the experience of setting up Stable Diffusion with Core ML locally on a Mac is aimed at developers and requires some basic command-line skills, but Hugging Face published an in-depth guide to setting Apple’s Core ML optimizations for those who want to experiment.

    For those less technically inclined, the previously mentioned app called Diffusion Bee makes it easy to run Stable Diffusion on Apple Silicon, but it does not integrate Apple’s new optimizations yet. Also, you can run Stable Diffusion on an iPhone or iPad using the Draw Things app.

  • 5 Common Image Editing Errors and How to Avoid Them

    5 Common Image Editing Errors and How to Avoid Them

    Everyone makes mistakes, even when it comes to image editing.

    Advancements in technology have expanded the ways we use images in our daily lives, from social media posts to academic research. At the same time, people have grown increasingly reliant on technology, making mistakes all the more likely. It is essential to be aware of some of the most frequent image editing errors to avoid them in the future. Read on to learn about common image editing errors you must watch out for!

    1. Over-Saturation

    Oversaturation is a common image editing error that can occur when too image tones and colors are added to an image. It can often make an image look fake or unnatural. 

    To avoid this, you need to be careful when editing your image and not go overboard with the color settings. Instead, try creating a natural and balanced look using a more subdued color palette.

    2. Over or Under-Exposing Your Images

    This can be a problem in editing website images because it can make the subject look too light or too dark. There are a few ways to avoid this problem.

    First, you can ensure that you use the correct exposure settings when taking the photo. Then, you can edit the photo in RAW format to have more control over the exposure.

    You can also use an editing program that has exposure image editing tools. Make sure to avoid under-exposing or over-exposing your images using one or all of these methods.

    3. Not Paying Attention to Backgrounds

    A common image editing error is not paying attention to backgrounds. This can result in a photo that is not well composed and looks cluttered. To avoid this error, take a moment to look at the background before taking the photo.

    Make sure nothing in the background will take away from the photo’s subject. If there is something in the background that you don’t want in the photo, move the camera to get a better composition.

    4. Cropping Mistakes

    You should learn how to crop appropriately to change the photo’s background. Leave enough breathing room around the subject, and trim evenly from all sides. Cropping too tightly might cut off important details or make the subject look unbalanced. 

    After cropping the subject to the desired proportions, you can use a background maker to create the right background for your subject so that it appears at its finest.

    5. Not Sharpening Your Images

    Not sharpening your images can make your images look softer and less defined. To avoid this error, make sure to sharpen your images after you have edited them.

    You can use several methods to sharpen your images, including an edge detection filter or an unsharp mask.

    Avoid These Common Image Editing Errors Today

    When working with images, save often, use Undo liberally, and take advantage of software features that can help you avoid mistakes.

    Whether you’re a beginner or a seasoned pro, we all make these common image editing errors from time to time. However, being aware of the most common errors can produce better results with your photos.

    So, the next time you’re in the editing room, keep these pointers in mind and see how avoiding these mistakes can improve your photos!

    Did you find this article helpful? Check out the rest of our blogs!

  • Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence

  • 1.

    Samuel, A. L. in Computer Games I (ed. Levy D.N.L.) 366–400 (Springer New York, 1988).

  • 2.

    Fletcher, K. H. Matter with a mind; a neurological research robot. Research 4, 305–307 (1951).

    CAS 
    PubMed 

    Google Scholar     

  • 3.

    Kononenko, I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23, 89–109 (2001).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 4.

    Kugelman, J. et al. Automatic choroidal segmentation in OCT images using supervised deep learning methods. Sci. Rep. 9, 13298 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar     

  • 5.

    Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation 234–241 (Springer International Publishing, Cham, 2015).

  • 6.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 7.

    Müller, P. L. et al. in High Resolution Imaging in Microscopy and Ophthalmology: New Frontiers in Biomedical Optics (ed. Bille, J. F.) 87–106 (Springer International Publishing, 2019).

  • 8.

    Huang, D. et al. Optical coherence tomography. Science 254, 1178–1181 (1991).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 9.

    Mrejen, S. & Spaide, R. F. Optical coherence tomography: imaging of the choroid and beyond. Surv. Ophthalmol. 58, 387–429 (2013).

    PubMed 
    Article 

    Google Scholar     

  • 10.

    Staurenghi, G., Sadda, S., Chakravarthy, U. & Spaide, R. F. Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography. Ophthalmology 121, 1572–1578 (2014).

  • 11.

    von der Emde, L. et al. Artificial intelligence for morphology-based function prediction in neovascular age-related macular degeneration. Sci. Rep. 9, 11132 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar     

  • 12.

    Lee, C. S., Baughman, D. M. & Lee, A. Y. Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Ophthalmol. Retina 1, 322–327 (2017).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 13.

    Motozawa, N. et al. Optical coherence tomography-based deep-learning models for classifying normal and age-related macular degeneration and exudative and non-exudative age-related macular degeneration changes. Ophthalmol. Ther. 8, 527–539 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 14.

    Keel, S. et al. Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology outpatient services: a pilot study. Sci. Rep. 8, 4330 (2018).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar     

  • 15.

    Bellemo, V. et al. Artificial intelligence screening for diabetic retinopathy: the real-world emerging application. Curr. Diab. Rep. 19, 72 (2019).

    PubMed 
    Article 

    Google Scholar     

  • 16.

    Grzybowski, A. et al. Artificial intelligence for diabetic retinopathy screening: a review. Eye 34, 451–460 (2020).

    PubMed 
    Article 

    Google Scholar     

  • 17.

    Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402–2410 (2016).

    Article 
    PubMed 

    Google Scholar     

  • 18.

    Arcadu, F. et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit. Med. 2, 92 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 19.

    Waldstein, S. M. et al. Evaluating the impact of vitreomacular adhesion on anti-VEGF therapy for retinal vein occlusion using machine learning. Sci. Rep. 7, 2928 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar     

  • 20.

    Schlegl, T. et al. Fully automated detection and quantification of macular fluid in OCT using deep learning. Ophthalmology 125, 549–558 (2018).

    PubMed 
    Article 

    Google Scholar     

  • 21.

    Zutis, K. et al. Towards automatic detection of abnormal retinal capillaries in ultra-widefield-of-view retinal angiographic exams. In Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. Vol. 2013, 7372–7375 (Osaka, Japan, 2013).

  • 22.

    Müller, P. L. et al. Prediction of function in ABCA4-related retinopathy using Ensemble machine learning. J. Clin. Med. 9, 2428 (2020).

    PubMed Central 
    Article 
    CAS 

    Google Scholar     

  • 23.

    De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).

    PubMed 
    Article 
    CAS 

    Google Scholar     

  • 24.

    Maloca, P. M. et al. Validation of automated artificial intelligence segmentation of optical coherence tomography images. PLoS ONE 14, e0220063 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 25.

    Quellec, G. et al. Feasibility of support vector machine learning in age-related macular degeneration using small sample yielding sparse optical coherence tomography data. Acta Ophthalmol. 97, e719–e728 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 26.

    Darcy, A. M., Louie, A. K. & Roberts, L. W. Machine learning and the profession of medicine. JAMA 315, 551–552 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 27.

    Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 1–47 (2018).

  • 28.

    Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 29.

    King, B. F. Artificial intelligence and radiology: what will the future hold? J. Am. Coll. Radiol. 15, 501–503 (2018).

    PubMed 
    Article 

    Google Scholar     

  • 30.

    Coiera, E. The fate of medicine in the time of AI. Lancet 392, 2331–2332 (2018).

    PubMed 
    Article 

    Google Scholar     

  • 31.

    Jha, S. & Topol, E. J. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316, 2353–2354 (2016).

    PubMed 
    Article 

    Google Scholar     

  • 32.

    Makridakis, S. The forthcoming artificial intelligence (AI) revolution: its impact on society and firms. Futures 90, 46–60 (2017).

    Article 

    Google Scholar     

  • 33.

    Silver, D. et al. Mastering the game of Go with deep neural networks and tree search. Nature 529, 484–489 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 34.

    Chan, S. & Siegel, E. L. Will machine learning end the viability of radiology as a thriving medical specialty? Br. J. Radiol. 92, 20180416 (2019).

    PubMed 
    Article 

    Google Scholar     

  • 35.

    Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 36.

    Ferrucci, D., Levas, A., Bagchi, S., Gondek, D. & Mueller, E. T. Watson: beyond jeopardy! Artif. Intell. 199–200, 93–105 (2013).

    Article 

    Google Scholar     

  • 37.

    Liu, X. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1, e271–e297 (2019).

    PubMed 
    Article 

    Google Scholar     

  • 38.

    Bouwmeester, W. et al. Reporting and methods in clinical prediction research: a systematic review. PLoS Med. 9, 1–12 (2012).

    PubMed 
    Article 

    Google Scholar     

  • 39.

    Collins, G. S. & Moons, K. G. M. Reporting of artificial intelligence prediction models. Lancet 393, 1577–1579 (2019).

    PubMed 
    Article 

    Google Scholar     

  • 40.

    Schulz, K. F., Altman, D. G., Moher, D. & CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ 340, c332 (2010).

  • 41.

    Calvert, M. et al. Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: the SPIRIT-PRO Extension. JAMA 319, 483–494 (2018).

    PubMed 
    Article 

    Google Scholar     

  • 42.

    CONSORT-AI and SPIRIT-AI Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat. Med. 25, 1467–1468 (2019).

    Article 
    CAS 

    Google Scholar     

  • 43.

    Liu, X., Faes, L., Calvert, M. J., Denniston, A. K. & CONSORT/SPIRIT-AI Extension Group. Extension of the CONSORT and SPIRIT statements. Lancet 394, 1225 (2019).

  • 44.

    Kaiser, T. M. & Burger, P. B. Error tolerance of machine learning algorithms across contemporary biological targets. Molecules 24, https://doi.org/10.3390/molecules24112115 (2019).

  • 45.

    Beam, A. L., Manrai, A. K. & Ghassemi, M. Challenges to the reproducibility of machine learning models in health care. JAMA 323, 305–306 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 46.

    Ting, D. S. W. et al. Artificial intelligence and deep learning in ophthalmology. Br. J. Ophthalmol. 103, 167–175 (2019).

    PubMed 
    Article 

    Google Scholar     

  • 47.

    Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015).

    PubMed 
    Article 

    Google Scholar     

  • 48.

    Castelvecchi, D. Can we open the black box of AI? Nature 538, 20–23 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 49.

    Guidotti, R. et al. A survey of methods for explaining black box models. ACM Comput. Surv. 51, 1–42 (2019).

    Article 

    Google Scholar     

  • 50.

    Lipton, Z. C. The mythos of model interpretability. Queue 16, 31–57 (2018).

    Article 

    Google Scholar     

  • 51.

    Gunning, D. & Aha, D. DARPA’s explainable artificial intelligence (XAI) program. AI Mag. 40, 44–58 (2019).

    Article 

    Google Scholar     

  • 52.

    Holzinger, A., Kieseberg, P., Weippl, E. & Tjoa, A. M. Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI. In Machine Learning and Knowledge Extraction. CD-MAKE 2018. Lecture Notes in Computer Science, Vol 11015, 1–8 (eds. Holzinger, A. et al.) (Springer, Cham., 2018). https://doi.org/10.1007/978-3-319-99740-7_1.

  • 53.

    Barredo Arrieta, A. et al. Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020).

    Article 

    Google Scholar     

  • 54.

    Montavon, G., Samek, W. & Müller, K.-R. Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1–15 (2018).

    Article 

    Google Scholar     

  • 55.

    Holzinger, A., Langs, G., Denk, H., Zatloukal, K. & Müller, H. Causability and explainability of artificial intelligence in medicine. WIREs Data Min. Knowl. Discov. 9, e1312. https://doi.org/10.1002/widm.1312 (2019).

  • 56.

    Holzinger, A., Carrington, A. & Müller, H. Measuring the quality of explanations: The System Causability Scale (SCS). KI K.ünstliche Intell. 34, 193–198 (2020).

    Article 

    Google Scholar     

  • 57.

    Ribeiro, M. T., Singh, S. & Guestrin, C. Why Should I. Trust You? In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1135–1144 (ACM, 2016).

  • 58.

    Lakkaraju, H., Kamar, E., Caruana, R. & Leskovec, J. Interpretable & explorable approximations of black box models. Preprint at https://arxiv.org/abs/1707.01154 (2017).

  • 59.

    Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vis. https://doi.org/10.1007/s11263-019-01228-7 (2016).

  • 60.

    Wickstrom, K., Kampffmeyer, M. & Jenssen, R. Uncertainty modeling and interpretability in convolutional neural networks for polyp segmentation. In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) 1–6 (IEEE, 2018).

  • 61.

    Vinogradova, K., Dibrov, A. & Myers, G. Towards Interpretable semantic segmentation via gradient-weighted class activation mapping. Preprint at https://arxiv.org/abs/2002.11434 (2020).

  • 62.

    Bach, S. et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10, e0130140 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar     

  • 63.

    Seegerer, P. et al. Interpretable deep neural network to predict estrogen receptor status from haematoxylin-eosin images. in Artificial Intelligence and Machine Learning for Digital Pathology (eds. Holzinger, A. et al.) 16–37 (Springer, Cham, 2020).

  • 64.

    Montavon, G., Lapuschkin, S., Binder, A., Samek, W. & Müller, K.-R. Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit. 65, 211–222 (2017).

    Article 

    Google Scholar     

  • 65.

    Kim, B. et al. Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). in Proceedings of the 35th International Conference on Machine Learning, Vol. 80 (eds Jennifer, D. & Andreas, K.) 2668–2677 (PMLR, Proceedings of Machine Learning Research, 2018).

  • 66.

    Moussa, M. et al. Grading of macular perfusion in retinal vein occlusion using en-face swept-source optical coherence tomography angiography: a retrospective observational case series. BMC Ophthalmol. 19, 127 (2019).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 67.

    Swanson, E. A. & Fujimoto, J. G. The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact [Invited]. Biomed. Opt. Express 8, 1638 (2017).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 68.

    Holz, F. G. et al. Multi-country real-life experience of anti-vascular endothelial growth factor therapy for wet age-related macular degeneration. Br. J. Ophthalmol. 99, 220–226 (2015).

    PubMed 
    Article 

    Google Scholar     

  • 69.

    Alshareef, R. A. et al. Segmentation errors in macular ganglion cell analysis as determined by optical coherence tomography in eyes with macular pathology. Int. J. Retin. Vitr. 3, 25 (2017).

    Article 

    Google Scholar     

  • 70.

    Al-Sheikh, M., Ghasemi Falavarjani, K., Akil, H. & Sadda, S. R. Impact of image quality on OCT angiography based quantitative measurements. Int. J. Retina Vitreous 3, 13 (2017).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 71.

    Sadda, S. R. et al. Errors in retinal thickness measurements obtained by optical coherence tomography. Ophthalmology 113, 285–293 (2006).

    PubMed 
    Article 

    Google Scholar     

  • 72.

    Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2015).

  • 73.

    Sinz, F. H., Pitkow, X., Reimer, J., Bethge, M. & Tolias, A. S. Engineering a less artificial intelligence. Neuron 103, 967–979 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 74.

    Zador, A. M. A critique of pure learning and what artificial neural networks can learn from animal brains. Nat. Commun. 10, 3770 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar     

  • 75.

    Tajmir, S. H. et al. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skelet. Radio. 48, 275–283 (2019).

    Article 

    Google Scholar     

  • 76.

    Kellner-Weldon, F. et al. Comparison of perioperative automated versus manual two-dimensional tumor analysis in glioblastoma patients. Eur. J. Radiol. 95, 75–81 (2017).

    PubMed 
    Article 

    Google Scholar     

  • 77.

    Ma, Z., Turrigiano, G. G., Wessel, R. & Hengen, K. B. Cortical circuit dynamics are homeostatically tuned to criticality in vivo. Neuron 104, 655–664.e4 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 78.

    Shibayama, S. & Wang, J. Measuring originality in science. Scientometrics 122, 409–427 (2020).

    Article 

    Google Scholar     

  • 79.

    Dirk, L. A measure of originality. Soc. Stud. Sci. 29, 765–776 (1999).

    Article 

    Google Scholar     

  • 80.

    Hägele, M. et al. Resolving challenges in deep learning-based analyses of histopathological images using explanation methods. Sci. Rep. 10, 6423 (2020).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar     

  • 81.

    Panwar, H. et al. A deep learning and grad-CAM based color visualization approach for fast detection of COVID-19 cases using chest X-ray and CT-Scan images. Chaos Solitons Fractals 140, 110190 (2020).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar     

  • 82.

    Anger, E. M. et al. Ultrahigh resolution optical coherence tomography of the monkey fovea. Identification of retinal sublayers by correlation with semithin histology sections. Exp. Eye Res. 78, 1117–1125 (2004).

    CAS 
    PubMed 
    Article 

    Google Scholar     

  • 83.

    Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249–256 (2010).


    Google Scholar     

  • 84.

    Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization CoRR abs/1412.6980 (2015).

  • 85.

    Litjens, G. et al. A survey on deep learning in medical image analysis. Med. Image Anal. https://doi.org/10.1016/j.media.2017.07.005 (2017).

  • 86.

    Kosub, S. A note on the triangle inequality for the Jaccard distance arXiv:1612.02696 (2016).

  • 87.

    Borg, I. & Groenen, P. Modern Multidimensional Scaling (Springer New York, 1997).

  • 88.

    R Core Team. R: A Language and Environment for Statistical Computing (2019).

  • 89.

    Fay, M. P. & Shaw, P. A. Exact and asymptotic weighted logrank tests for interval censored data: the interval R package. J. Stat. Softw. 36 (2010).

  • 90.

    Maloca, M. P. et al. Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence. Code/software v1.0. https://doi.org/10.5281/zenodo.4380269 (2020).