Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

In safety-critical applications like medical diagnosis, certainty associated with a model's prediction is just as important as its accuracy. Consequently, uncertainty estimation and reduction play a crucial role. Uncertainty in predictions can be attributed to noise or randomness in data (aleatoric) and incorrect model inferences (epistemic). While model uncertainty can be reduced with more data or bigger models, aleatoric uncertainty is more intricate. This work proposes a novel approach that interprets data uncertainty estimated from a self-supervised task as noise inherent to the data and utilizes it to reduce aleatoric uncertainty in another task related to the same dataset via data augmentation. The proposed method was evaluated on a benchmark medical imaging dataset with image reconstruction as the self-supervised task and segmentation as the image analysis task. Our findings demonstrate the effectiveness of the proposed approach in significantly reducing the aleatoric uncertainty in the image segmentation task while achieving better or on-par performance compared to the standard augmentation techniques.Comment: Accepted in IEEE International Symposium on Biomedical Imaging (ISBI) 202

Understanding Calibration of Deep Neural Networks for Medical Image Classification

In the field of medical image analysis, achieving high accuracy is not enough; ensuring well-calibrated predictions is also crucial. Confidence scores of a deep neural network play a pivotal role in explainability by providing insights into the model's certainty, identifying cases that require attention, and establishing trust in its predictions. Consequently, the significance of a well-calibrated model becomes paramount in the medical imaging domain, where accurate and reliable predictions are of utmost importance. While there has been a significant effort towards training modern deep neural networks to achieve high accuracy on medical imaging tasks, model calibration and factors that affect it remain under-explored. To address this, we conducted a comprehensive empirical study that explores model performance and calibration under different training regimes. We considered fully supervised training, which is the prevailing approach in the community, as well as rotation-based self-supervised method with and without transfer learning, across various datasets and architecture sizes. Multiple calibration metrics were employed to gain a holistic understanding of model calibration. Our study reveals that factors such as weight distributions and the similarity of learned representations correlate with the calibration trends observed in the models. Notably, models trained using rotation-based self-supervised pretrained regime exhibit significantly better calibration while achieving comparable or even superior performance compared to fully supervised models across different medical imaging datasets. These findings shed light on the importance of model calibration in medical image analysis and highlight the benefits of incorporating self-supervised learning approach to improve both performance and calibration.Comment: Accepted in Computer Methods and Programs in Biomedicine Journa

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

[EN] Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MIC-CAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.This work was supported by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, J.I.O is supported by WWTF (Medical University of Vienna: AugUniWien/FA7464A0249, University of Vienna: VRG12- 009). Team Masker is supported by Natural Science Foundation of Guangdong Province of China (Grant 2017A030310647). Team BUCT is partially supported by the National Natural Science Foundation of China (Grant 11571031). The authors would also like to thank REFUGE study group for collaborating with this challenge.Orlando, JI.; Fu, H.; Breda, JB.; Van Keer, K.; Bathula, DR.; Diaz-Pinto, A.; Fang, R.... (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis. 59:1-21. https://doi.org/10.1016/j.media.2019.101570S12159Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 3, 169-208. doi:10.1109/rbme.2010.2084567Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0040-6Al-Bander, B., Williams, B., Al-Nuaimy, W., Al-Taee, M., Pratt, H., & Zheng, Y. (2018). Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry, 10(4), 87. doi:10.3390/sym10040087Almazroa, A., Burman, R., Raahemifar, K., & Lakshminarayanan, V. (2015). Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey. Journal of Ophthalmology, 2015, 1-28. doi:10.1155/2015/180972Burlina, P. M., Joshi, N., Pekala, M., Pacheco, K. D., Freund, D. E., & Bressler, N. M. (2017). Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmology, 135(11), 1170. doi:10.1001/jamaophthalmol.2017.3782Carmona, E. J., Rincón, M., García-Feijoó, J., & Martínez-de-la-Casa, J. M. (2008). Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine, 43(3), 243-259. doi:10.1016/j.artmed.2008.04.005Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Christopher, M., Belghith, A., Bowd, C., Proudfoot, J. A., Goldbaum, M. H., Weinreb, R. N., … Zangwill, L. M. (2018). Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. Scientific Reports, 8(1). doi:10.1038/s41598-018-35044-9De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., … Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350. doi:10.1038/s41591-018-0107-6Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., … Klein, J.-C. (2014). FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis & Stereology, 33(3), 231. doi:10.5566/ias.1155DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics, 44(3), 837. doi:10.2307/2531595European Glaucoma Society Terminology and Guidelines for Glaucoma, 4th Edition - Part 1Supported by the EGS Foundation. (2017). British Journal of Ophthalmology, 101(4), 1-72. doi:10.1136/bjophthalmol-2016-egsguideline.001Farbman, Z., Fattal, R., Lischinski, D., & Szeliski, R. (2008). Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Transactions on Graphics, 27(3), 1-10. doi:10.1145/1360612.1360666Fu, H., Cheng, J., Xu, Y., Wong, D. W. K., Liu, J., & Cao, X. (2018). Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation. IEEE Transactions on Medical Imaging, 37(7), 1597-1605. doi:10.1109/tmi.2018.2791488Gómez-Valverde, J. J., Antón, A., Fatti, G., Liefers, B., Herranz, A., Santos, A., … Ledesma-Carbayo, M. J. (2019). Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning. Biomedical Optics Express, 10(2), 892. doi:10.1364/boe.10.000892Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., … Webster, D. R. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402. doi:10.1001/jama.2016.17216Hagiwara, Y., Koh, J. E. W., Tan, J. H., Bhandary, S. V., Laude, A., Ciaccio, E. J., … Acharya, U. R. (2018). Computer-aided diagnosis of glaucoma using fundus images: A review. Computer Methods and Programs in Biomedicine, 165, 1-12. doi:10.1016/j.cmpb.2018.07.012Haleem, M. S., Han, L., van Hemert, J., & Li, B. (2013). Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review. Computerized Medical Imaging and Graphics, 37(7-8), 581-596. doi:10.1016/j.compmedimag.2013.09.005Holm, S., Russell, G., Nourrit, V., & McLoughlin, N. (2017). DR HAGIS—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients. Journal of Medical Imaging, 4(1), 014503. doi:10.1117/1.jmi.4.1.014503Joshi, G. D., Sivaswamy, J., & Krishnadas, S. R. (2011). Optic Disk and Cup Segmentation From Monocular Color Retinal Images for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 30(6), 1192-1205. doi:10.1109/tmi.2011.2106509Kaggle, 2015. Diabetic Retinopathy Detection. https://www.kaggle.com/c/diabetic-retinopathy-detection. [Online; accessed 10-January-2019].Kumar, J. R. H., Seelamantula, C. S., Kamath, Y. S., & Jampala, R. (2019). Rim-to-Disc Ratio Outperforms Cup-to-Disc Ratio for Glaucoma Prescreening. Scientific Reports, 9(1). doi:10.1038/s41598-019-43385-2Lavinsky, F., Wollstein, G., Tauber, J., & Schuman, J. S. (2017). The Future of Imaging in Detecting Glaucoma Progression. Ophthalmology, 124(12), S76-S82. doi:10.1016/j.ophtha.2017.10.011Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. doi:10.1109/5.726791Li, Z., He, Y., Keel, S., Meng, W., Chang, R. T., & He, M. (2018). Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology, 125(8), 1199-1206. doi:10.1016/j.ophtha.2018.01.023Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88. doi:10.1016/j.media.2017.07.005Liu, S., Graham, S. L., Schulz, A., Kalloniatis, M., Zangerl, B., Cai, W., … You, Y. (2018). A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs. Ophthalmology Glaucoma, 1(1), 15-22. doi:10.1016/j.ogla.2018.04.002Lowell, J., Hunter, A., Steel, D., Basu, A., Ryder, R., Fletcher, E., & Kennedy, L. (2004). Optic Nerve Head Segmentation. IEEE Transactions on Medical Imaging, 23(2), 256-264. doi:10.1109/tmi.2003.823261Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., … Kopp-Schneider, A. (2018). Why rankings of biomedical image analysis competitions should be interpreted with care. Nature Communications, 9(1). doi:10.1038/s41467-018-07619-7Miri, M. S., Abramoff, M. D., Lee, K., Niemeijer, M., Wang, J.-K., Kwon, Y. H., & Garvin, M. K. (2015). Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach. IEEE Transactions on Medical Imaging, 34(9), 1854-1866. doi:10.1109/tmi.2015.2412881Niemeijer, M., van Ginneken, B., Cree, M. J., Mizutani, A., Quellec, G., Sanchez, C. I., … Abramoff, M. D. (2010). Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging, 29(1), 185-195. doi:10.1109/tmi.2009.2033909Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., … Angelopoulou, E. (2013). Retinal vessel segmentation by improved matched filtering: evaluation on a new high‐resolution fundus image database. IET Image Processing, 7(4), 373-383. doi:10.1049/iet-ipr.2012.0455Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017). A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Transactions on Biomedical Engineering, 64(1), 16-27. doi:10.1109/tbme.2016.2535311Park, S. J., Shin, J. Y., Kim, S., Son, J., Jung, K.-H., & Park, K. H. (2018). A Novel Fundus Image Reading Tool for Efficient Generation of a Multi-dimensional Categorical Image Database for Machine Learning Algorithm Training. Journal of Korean Medical Science, 33(43). doi:10.3346/jkms.2018.33.e239Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., … Webster, D. R. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158-164. doi:10.1038/s41551-018-0195-0Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., & Meriaudeau, F. (2018). Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research. Data, 3(3), 25. doi:10.3390/data3030025Prokofyeva, E., & Zrenner, E. (2012). Epidemiology of Major Eye Diseases Leading to Blindness in Europe: A Literature Review. Ophthalmic Research, 47(4), 171-188. doi:10.1159/000329603Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., & Acharya, U. R. (2018). Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences, 441, 41-49. doi:10.1016/j.ins.2018.01.051Reis, A. S. C., Sharpe, G. P., Yang, H., Nicolela, M. T., Burgoyne, C. F., & Chauhan, B. C. (2012). Optic Disc Margin Anatomy in Patients with Glaucoma and Normal Controls with Spectral Domain Optical Coherence Tomography. Ophthalmology, 119(4), 738-747. doi:10.1016/j.ophtha.2011.09.054Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., … Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision, 115(3), 211-252. doi:10.1007/s11263-015-0816-ySchmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018). Artificial intelligence in retina. Progress in Retinal and Eye Research, 67, 1-29. doi:10.1016/j.preteyeres.2018.07.004Sevastopolsky, A. (2017). Optic disc and cup segmentation methods for glaucoma detection with modification of U-Net convolutional neural network. Pattern Recognition and Image Analysis, 27(3), 618-624. doi:10.1134/s1054661817030269Taha, A. A., & Hanbury, A. (2015). Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Medical Imaging, 15(1). doi:10.1186/s12880-015-0068-xThakur, N., & Juneja, M. (2018). Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42, 162-189. doi:10.1016/j.bspc.2018.01.014Tham, Y.-C., Li, X., Wong, T. Y., Quigley, H. A., Aung, T., & Cheng, C.-Y. (2014). Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040. Ophthalmology, 121(11), 2081-2090. doi:10.1016/j.ophtha.2014.05.013Johnson, S. S., Wang, J.-K., Islam, M. S., Thurtell, M. J., Kardon, R. H., & Garvin, M. K. (2018). Local Estimation of the Degree of Optic Disc Swelling from Color Fundus Photography. Lecture Notes in Computer Science, 277-284. doi:10.1007/978-3-030-00949-6_33Trucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J. P., … Dhillon, B. (2013). Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal. Investigative Opthalmology & Visual Science, 54(5), 3546. doi:10.1167/iovs.12-10347Vergara, I. A., Norambuena, T., Ferrada, E., Slater, A. W., & Melo, F. (2008). StAR: a simple tool for the statistical comparison of ROC curves. BMC Bioinformatics, 9(1). doi:10.1186/1471-2105-9-265Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or Deeper: Revisiting the ResNet Model for Visual Recognition. Pattern Recognition, 90, 119-133. doi:10.1016/j.patcog.2019.01.006Zheng, Y., Hijazi, M. H. A., & Coenen, F. (2012). Automated «Disease/No Disease» Grading of Age-Related Macular Degeneration by an Image Mining Approach. Investigative Opthalmology & Visual Science, 53(13), 8310. doi:10.1167/iovs.12-957

Predicting women with depressive symptoms postpartum with machine learning methods

Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high-risk women, but our ability to identify these is poor. We leveraged the power of clinical, demographic, and psychometric data to assess if machine learning methods can make accurate predictions of postpartum depression. Data were obtained from a population-based prospective cohort study in Uppsala, Sweden, collected between 2009 and 2018 (BASIC study, n = 4313). Sub-analyses among women without previous depression were performed. The extremely randomized trees method provided robust performance with highest accuracy and well-balanced sensitivity and specificity (accuracy 73%, sensitivity 72%, specificity 75%, positive predictive value 33%, negative predictive value 94%, area under the curve 81%). Among women without earlier mental health issues, the accuracy was 64%. The variables setting women at most risk for PPD were depression and anxiety during pregnancy, as well as variables related to resilience and personality. Future clinical models that could be implemented directly after delivery might consider including these variables in order to identify women at high risk for postpartum depression to facilitate individualized follow-up and cost-effectiveness

Learning Interpretable Regularized Ordinal Models from 3D Mesh Data for Neurodegenerative Disease Staging

We extend the sparse, spatially piecewise-contiguous linear classification framework for mesh-based data to ordinal logistic regression. The algorithm is intended for use with subcortical shape and cortical thickness data where progressive clinical staging is available, as is generally the case in neurodegenerative diseases. We apply the tool to Parkinson’s and Alzheimer’s disease staging. The resulting biomarkers predict Hoehn-Yahr and cognitive impairment stages at competitive accuracy; the models remain parsimonious and outperform one-against-all models in terms of the Akaike and Bayesian information criteria

Machine learning in clinical neuroimaging

This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track).The rise of neuroimaging data, bolstered by the rapid advancements in computational resources and algorithms, is poised to drive significant breakthroughs in clinical neuroscience. Notably, deep learning is gaining relevance in this domain. Yet, there’s an imbalance: while computational methods grow in complexity, the breadth and diversity of standard evaluation datasets lag behind. This mismatch could result in findings that don’t generalize to a wider population or are skewed towards dominant groups. To address this, it’s imperative to foster inter-domain collaborations that move state-of-the art methods quickly into clinical research. Bridging the divide between various specialties can pave the way for methodological innovations to smoothly transition into clinical research and ultimately, real-world applications.Ourworkshop aimed to facilitate this by creating a forum for dialogue among engineers, clinicians, and neuroimaging specialists. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) was held on October 8th, 2023, as a satellite event of the 26th International Conference on Medical Imaging Computing & Computer-Assisted Intervention (MICCAI 2023) in Vancouver to continue the yearly recurring dialog between experts in machine learning and clinical neuroimaging. The call for papers was made on May 2nd, 2023, and submissions were closed on July 4th, 2023. Each of the 27 submitted manuscripts was reviewed by three or more program committee members in a double-blinded review process. The sixteen accepted papers showcase the integration of machine learning techniques with clinical neuroimaging data. Studied clinical conditions include Alzheimer’s disease, autism spectrum disorder, stroke, and aging. There is a strong emphasis on deep learning approaches to analysis of structural and functional MRI, positron emission tomography, and computed tomography. Research also delves into multi-modal data synthesis and analysis. The conference encapsulated the blend of methodological innovation and clinical applicability in neuroimaging. The proceedings mirror the hallmarks in the sections “Machine learning” and “Clinical applications”, although all papers carry clinical relevance and provide methodological novelty. For the sixth time, this workshop was put together by a dedicated community of authors, program committee, steering committee, and workshop participants. We thank all creators and attendees for their valuable contributions that made the MLCN 2023 Workshop a success