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Book ; Online: Learning to Compare Longitudinal Images

Kim, Heejong / Sabuncu, Mert R.

2023

Abstract: Longitudinal studies, where a series of images from the same set of individuals are acquired at different time-points, represent a popular technique for studying and characterizing temporal dynamics in biomedical applications. The classical approach for ... ...

Abstract	Longitudinal studies, where a series of images from the same set of individuals are acquired at different time-points, represent a popular technique for studying and characterizing temporal dynamics in biomedical applications. The classical approach for longitudinal comparison involves normalizing for nuisance variations, such as image orientation or contrast differences, via pre-processing. Statistical analysis is, in turn, conducted to detect changes of interest, either at the individual or population level. This classical approach can suffer from pre-processing issues and limitations of the statistical modeling. For example, normalizing for nuisance variation might be hard in settings where there are a lot of idiosyncratic changes. In this paper, we present a simple machine learning-based approach that can alleviate these issues. In our approach, we train a deep learning model (called PaIRNet, for Pairwise Image Ranking Network) to compare pairs of longitudinal images, with or without supervision. In the self-supervised setup, for instance, the model is trained to temporally order the images, which requires learning to recognize time-irreversible changes. Our results from four datasets demonstrate that PaIRNet can be very effective in localizing and quantifying meaningful longitudinal changes while discounting nuisance variation. Our code is available at \url{https://github.com/heejong-kim/learning-to-compare-longitudinal-images.git} Comment: to be published in MIDL 2023
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2023-04-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Intelligence plays dice

Sabuncu, Mert R.

Stochasticity is essential for machine learning

2020

Abstract: Many fields view stochasticity as a way to gain computational efficiency, while often having to trade off accuracy. In this perspective article, we argue that stochasticity plays a fundamentally different role in machine learning (ML) and is likely a ... ...

Abstract	Many fields view stochasticity as a way to gain computational efficiency, while often having to trade off accuracy. In this perspective article, we argue that stochasticity plays a fundamentally different role in machine learning (ML) and is likely a critical ingredient of intelligent systems. As we review the ML literature, we notice that stochasticity features in many ML methods, affording them robustness, generalizability, and calibration. We also note that randomness seems to be prominent in biological intelligence, from the spiking patterns of individual neurons to the complex behavior of animals. We conclude with a discussion of how we believe stochasticity might shape the future of ML.
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Publishing date	2020-08-17
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation.

Rajendran, Suraj / Pan, Weishen / Sabuncu, Mert R / Chen, Yong / Zhou, Jiayu / Wang, Fei

Patterns (New York, N.Y.)

2024 Volume 5, Issue 2, Page(s) 100913

Abstract: In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity ... ...

Abstract	In healthcare, machine learning (ML) shows significant potential to augment patient care, improve population health, and streamline healthcare workflows. Realizing its full potential is, however, often hampered by concerns about data privacy, diversity in data sources, and suboptimal utilization of different data modalities. This review studies the utility of cross-cohort cross-category (C
Language	English
Publishing date	2024-01-17
Publishing country	United States
Document type	Journal Article ; Review
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2023.100913
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Article: Release the Krakencoder: A unified brain connectome translation and fusion tool.

Jamison, Keith W / Gu, Zijin / Wang, Qinxin / Sabuncu, Mert R / Kuceyeski, Amy

bioRxiv : the preprint server for biology

2024

Abstract: Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional ... ...

Abstract	Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here we present the Krakencoder, a joint connectome mapping tool that simultaneously, bidirectionally translates between structural (SC) and functional connectivity (FC), and across different atlases and processing choices via a common latent representation. These mappings demonstrate unprecedented accuracy and individual-level identifiability; the mapping between SC and FC has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This "fusion" representation i) better reflects familial relatedness, ii) preserves age- and sex-relevant information and iii) enhances cognition-relevant information. The Krakencoder can be applied without retraining to new, out-of-age-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a significant leap forward in capturing the relationship between multi-modal brain connectomes in an individualized, behaviorally- and demographically-relevant way.
Language	English
Publishing date	2024-04-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.04.12.589274
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Book ; Online: A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification

Wang, Alan Q. / Sabuncu, Mert R.

2022

Abstract: In this paper, we empirically analyze a simple, non-learnable, and nonparametric Nadaraya-Watson (NW) prediction head that can be used with any neural network architecture. In the NW head, the prediction is a weighted average of labels from a support set. ...

Abstract	In this paper, we empirically analyze a simple, non-learnable, and nonparametric Nadaraya-Watson (NW) prediction head that can be used with any neural network architecture. In the NW head, the prediction is a weighted average of labels from a support set. The weights are computed from distances between the query feature and support features. This is in contrast to the dominant approach of using a learnable classification head (e.g., a fully-connected layer) on the features, which can be challenging to interpret and can yield poorly calibrated predictions. Our empirical results on an array of computer vision tasks demonstrate that the NW head can yield better calibration with comparable accuracy compared to its parametric counterpart, particularly in data-limited settings. To further increase inference-time efficiency, we propose a simple approach that involves a clustering step run on the training set to create a relatively small distilled support set. Furthermore, we explore two means of interpretability/explainability that fall naturally from the NW head. The first is the label weights, and the second is our novel concept of the ``support influence function,'' which is an easy-to-compute metric that quantifies the influence of a support element on the prediction for a given query. As we demonstrate in our experiments, the influence function can allow the user to debug a trained model. We believe that the NW head is a flexible, interpretable, and highly useful building block that can be used in a range of applications. Comment: Accepted to Transactions on Machine Learning Research (TMLR) 2023
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2022-12-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Human brain responses are modulated when exposed to optimized natural images or synthetically generated images.

Gu, Zijin / Jamison, Keith / Sabuncu, Mert R / Kuceyeski, Amy

Communications biology

2023 Volume 6, Issue 1, Page(s) 1076

Abstract: Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to images via functional MRI. In our first experiment, we found that images ... ...

Abstract	Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to images via functional MRI. In our first experiment, we found that images predicted to achieve maximal activations using a group level encoding model evoke higher responses than images predicted to achieve average activations, and the activation gain is positively associated with the encoding model accuracy. Furthermore, anterior temporal lobe face area (aTLfaces) and fusiform body area 1 had higher activation in response to maximal synthetic images compared to maximal natural images. In our second experiment, we found that synthetic images derived using a personalized encoding model elicited higher responses compared to synthetic images from group-level or other subjects' encoding models. The finding of aTLfaces favoring synthetic images than natural images was also replicated. Our results indicate the possibility of using data-driven and generative approaches to modulate macro-scale brain region responses and probe inter-individual differences in and functional specialization of the human visual system.
MeSH term(s)	Humans ; Brain/diagnostic imaging ; Brain/physiology ; Temporal Lobe/diagnostic imaging ; Temporal Lobe/physiology ; Brain Mapping/methods ; Magnetic Resonance Imaging/methods
Language	English
Publishing date	2023-10-23
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-023-05440-7
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Article ; Online: Modulating human brain responses via optimal natural image selection and synthetic image generation.

Gu, Zijin / Jamison, Keith / Sabuncu, Mert R / Kuceyeski, Amy

ArXiv

2023

Abstract: One of the main goals of neuroscience is to understand how biological brains interpret and process incoming environmental information. Building computational encoding models that map images to neural responses is one way to pursue this goal. Moreover, ... ...

Abstract	One of the main goals of neuroscience is to understand how biological brains interpret and process incoming environmental information. Building computational encoding models that map images to neural responses is one way to pursue this goal. Moreover, generating or selecting visual stimuli designed to achieve specific patterns of responses allows exploration and control of neuronal firing rates or regional brain activity responses. Here, we investigated the brain's regional activation selectivity and inter-individual differences in human brain responses to various sets of natural and synthetic (generated) images via two functional MRI (fMRI) studies. For our first fMRI study, we used a pre-trained group-level neural model for selecting or synthesizing images that are predicted to maximally activate targeted brain regions. We then presented these images to subjects while collecting their fMRI data. Our results show that optimized images indeed evoke larger magnitude responses than other images predicted to achieve average levels of activation.Furthermore, the activation gain is positively associated with the encoding model accuracy. While most regions' activations in response to maximal natural images and maximal synthetic images were not different, two regions, namely anterior temporal lobe faces (aTLfaces) and fusiform body area 1 (FBA1), had significantly higher activation in response to maximal synthetic images compared to maximal natural images. On the other hand, three regions; medial temporal lobe face area (mTLfaces), ventral word form area 1 (VWFA1) and ventral word form area 2 (VWFA2), had higher activation in response to maximal natural images compared to maximal synthetic images. In our second fMRI experiment, we focused on probing inter-individual differences in face regions' responses and found that individual-specific synthetic (and not natural) images derived using a personalized encoding model elicited significantly higher responses compared to synthetic images derived from the group-level or other subjects' encoding models. Finally, we replicated the finding showing synthetic images elicited larger activation responses in the aTLfaces region compared to natural image responses in that region. Here, for the first time, we leverage our data-driven and generative modeling framework NeuroGen to probe inter-individual differences in and functional specialization of the human visual system. Our results indicate that NeuroGen can be used to modulate macro-scale brain regions in specific individuals using synthetically generated visual stimuli.
Language	English
Publishing date	2023-04-18
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: Hyper-convolutions via implicit kernels for medical image analysis.

Ma, Tianyu / Wang, Alan Q / Dalca, Adrian V / Sabuncu, Mert R

Medical image analysis

2023 Volume 86, Page(s) 102796

Abstract: The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across ...

Abstract	The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels. A standard CNN's capacity, and thus its performance, is directly related to the number of learnable kernel weights, which is determined by the number of channels and the kernel size (support). In this paper, we present the hyper-convolution, a novel building block that implicitly encodes the convolutional kernel using spatial coordinates. Unlike a regular convolutional kernel, whose weights are independently learned, hyper-convolution kernel weights are correlated through an encoder that maps spatial coordinates to their corresponding values. Hyper-convolutions decouple kernel size from the total number of learnable parameters, enabling a more flexible architecture design. We demonstrate in our experiments that replacing regular convolutions with hyper-convolutions can improve performance with less parameters, and increase robustness against noise. We provide our code here: https://github.com/tym002/Hyper-Convolution.
MeSH term(s)	Humans ; Neural Networks, Computer ; Algorithms
Language	English
Publishing date	2023-03-16
Publishing country	Netherlands
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2023.102796
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Article ; Online: A robust and interpretable deep learning framework for multi-modal registration via keypoints.

Wang, Alan Q / Yu, Evan M / Dalca, Adrian V / Sabuncu, Mert R

Medical image analysis

2023 Volume 90, Page(s) 102962

Abstract: We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not ... ...

Abstract	We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression. We use this observation to drive the end-to-end learning of keypoints tailored for the registration task, and without knowledge of ground-truth keypoints. This framework not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the final alignment. Moreover, KeyMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. Finally, we show how multiple deformation fields can be computed efficiently and in closed-form at test time corresponding to different transformation variants. We demonstrate the proposed framework in solving 3D affine and spline-based registration of multi-modal brain MRI scans. In particular, we show registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at https://github.com/alanqrwang/keymorph.
MeSH term(s)	Humans ; Deep Learning ; Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Neuroimaging ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2023-09-13
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1356436-5
ISSN	1361-8423 ; 1361-8431 ; 1361-8415
ISSN (online)	1361-8423 ; 1361-8431
ISSN	1361-8415
DOI	10.1016/j.media.2023.102962
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Article ; Online: Machine learning based multi-modal prediction of future decline toward Alzheimer's disease: An empirical study.

Karaman, Batuhan K / Mormino, Elizabeth C / Sabuncu, Mert R

PloS one

2022 Volume 17, Issue 11, Page(s) e0277322

Abstract: Alzheimer's disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of ...

Abstract	Alzheimer's disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of this devastating disease. In this paper, we present an empirical study to characterize how predictable an individual subjects' future AD trajectory is, several years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our experiments demonstrate that our strategy yields predictions that are more accurate than a model trained on a single time horizon (e.g. 3 years), which is common practice in prior literature. We also provide a comparison between linear and nonlinear models, verifying the well-established insight that the latter can offer a boost in performance. Our results also confirm that predicting future decline for cognitively normal (CN) individuals is more challenging than for individuals with mild cognitive impairment (MCI). Intriguingly, however, we discover that prediction accuracy decreases with increasing time horizon for CN subjects, but the trend is in the opposite direction for MCI subjects. Additionally, we quantify the contribution of different data types in prediction, which yields novel insights into the utility of different biomarkers. We find that molecular biomarkers are not as helpful for CN individuals as they are for MCI individuals, whereas magnetic resonance imaging biomarkers (hippocampus volume, specifically) offer a significant boost in prediction accuracy for CN individuals. Finally, we show how our model's prediction reveals the evolution of individual-level progression risk over a five-year time horizon. Our code is available at https://github.com/batuhankmkaraman/mlbasedad.
MeSH term(s)	Humans ; Alzheimer Disease/diagnostic imaging ; Alzheimer Disease/pathology ; Cognitive Dysfunction/diagnostic imaging ; Cognitive Dysfunction/pathology ; Machine Learning ; Magnetic Resonance Imaging/methods ; Biomarkers ; Disease Progression
Chemical Substances	Biomarkers
Language	English
Publishing date	2022-11-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0277322
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