Camila Gonzalez

All Publications

• Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging. PRedictive Intelligence in MEdicine. PRIME (Workshop) Paschali, M., Jiang, Y. H., Siegel, S., Gonzalez, C., Pohl, K. M., Chaudhari, A., Zhao, Q. 2025; 15155: 24-34

  Abstract

  Recent advancements in medicine have confirmed that brain disorders often comprise multiple subtypes of mechanisms, developmental trajectories, or severity levels. Such heterogeneity is often associated with demographic aspects (e.g., sex) or disease-related contributors (e.g., genetics). Thus, the predictive power of machine learning models used for symptom prediction varies across subjects based on such factors. To model this heterogeneity, one can assign each training sample a factor-dependent weight, which modulates the subject's contribution to the overall objective loss function. To this end, we propose to model the subject weights as a linear combination of the eigenbases of a spectral population graph that captures the similarity of factors across subjects. In doing so, the learned weights smoothly vary across the graph, highlighting sub-cohorts with high and low predictability. Our proposed sample weighting scheme is evaluated on two tasks. First, we predict initiation of heavy alcohol drinking in young adulthood from imaging and neuropsychological measures from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA). Next, we detect Dementia vs. Mild Cognitive Impairment (MCI) using imaging and demographic measurements in subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Compared to existing sample weighting schemes, our sample weights improve interpretability and highlight sub-cohorts with distinct characteristics and varying model accuracy.

  View details for DOI 10.1007/978-3-031-74561-4_3

  View details for PubMedID 39525051

  View details for PubMedCentralID PMC11549025

• SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis. Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) Gonzalez, C., Miraoui, Y., Fan, Y., Adeli, E., Pohl, K. M. 2025; 15266: 46-56

  Abstract

  Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses, as the data is sensitive to scanning effects and inherently difficult to visualize. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision. Instead of extracting post-hoc feature attributions to uncover functional connections that are important to the target task, we identify a small subset of highly informative connections during training and occlude the rest. To this end, we jointly train a (1) sparse input mask, (2) variational autoencoder (VAE), and (3) downstream classifier in an end-to-end fashion. While we need a portion of labeled samples to train the classifier, we optimize the sparse mask and VAE with unlabeled data from additional acquisition sites, retaining only the input features that generalize well. We evaluate our method - Sparsely Reconstructed Graphs (SpaRG) - on the public ABIDE dataset for the task of sex classification, training with labeled cases from 18 sites and adapting the model to two additional out-of-distribution sites with a portion of unlabeled samples. For a relatively coarse parcellation (64 regions), SpaRG utilizes only 1% of the original connections while improving the classification accuracy across domains. Our code can be found at www.github.com/yanismiraoui/SpaRG.

  View details for DOI 10.1007/978-3-031-78761-4_5

  View details for PubMedID 39758707

• Multi-dimensional predictors of first drinking initiation and regular drinking onset in adolescence: A prospective longitudinal study. Developmental cognitive neuroscience Nguyen-Louie, T. T., Thompson, W. K., Sullivan, E. V., Pfefferbaum, A., Gonzalez, C., Eberson-Shumate, S. C., Wade, N. E., Clark, D. B., Nagel, B. J., Baker, F. C., Luna, B., Nooner, K. B., de Zambotti, M., Goldston, D. B., Knutson, B., Pohl, K. M., Tapert, S. F. 2024; 69: 101424

  Abstract

  Early adolescent drinking onset is linked to myriad negative consequences. Using the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) baseline to year 8 data, this study (1) leveraged best subsets selection and Cox Proportional Hazards regressions to identify the most robust predictors of adolescent first and regular drinking onset, and (2) examined the clinical utility of drinking onset in forecasting later binge drinking and withdrawal effects. Baseline predictors included youth psychodevelopmental characteristics, cognition, brain structure, family, peer, and neighborhood domains. Participants (N=538) were alcohol-naïve at baseline. The strongest predictors of first and regular drinking onset were positive alcohol expectancies (Hazard Ratios [HRs]=1.67-1.87), easy home alcohol access (HRs=1.62-1.67), more parental solicitation (e.g., inquiring about activities; HRs=1.72-1.76), and less parental control and knowledge (HRs=.72-.73). Robust linear regressions showed earlier first and regular drinking onset predicted earlier transition into binge and regular binge drinking (βs=0.57-0.95). Zero-inflated Poisson regressions revealed that delayed first and regular drinking increased the likelihood (Incidence Rate Ratios [IRR]=1.62 and IRR=1.29, respectively) of never experiencing withdrawal. Findings identified behavioral and environmental factors predicting temporal paths to youthful drinking, dissociated first from regular drinking initiation, and revealed adverse sequelae of younger drinking initiation, supporting efforts to delay drinking onset.

  View details for DOI 10.1016/j.dcn.2024.101424

  View details for PubMedID 39089172

• Lifelong nnU-Net: a framework for standardized medical continual learning. Scientific reports González, C., Ranem, A., Pinto Dos Santos, D., Othman, A., Mukhopadhyay, A. 2023; 13 (1): 9381

  Abstract

  As the enthusiasm surrounding Deep Learning grows, both medical practitioners and regulatory bodies are exploring ways to safely introduce image segmentation in clinical practice. One frontier to overcome when translating promising research into the clinical open world is the shift from static to continual learning. Continual learning, the practice of training models throughout their lifecycle, is seeing growing interest but is still in its infancy in healthcare. We present Lifelong nnU-Net, a standardized framework that places continual segmentation at the hands of researchers and clinicians. Built on top of the nnU-Net-widely regarded as the best-performing segmenter for multiple medical applications-and equipped with all necessary modules for training and testing models sequentially, we ensure broad applicability and lower the barrier to evaluating new methods in a continual fashion. Our benchmark results across three medical segmentation use cases and five continual learning methods give a comprehensive outlook on the current state of the field and signify a first reproducible benchmark.

  View details for DOI 10.1038/s41598-023-34484-2

  View details for PubMedID 37296233

  View details for PubMedCentralID PMC10256748

• Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata Information Processing in Medical Imaging Kalkhof, J., Gonzalez, C., Mukhopadhyay, A. 2023: 705--716

  View details for DOI 10.1007/978-3-031-34048-2_54

• Continual hippocampus segmentation with transformers IEEE/CVF Conference on Computer Vision and Pattern Recognition Ranem, A., Gonzalez, C., Mukhopadhyay, A. 2022: 3711--3720
• Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation Medical image analysis Gonzalez, C., Gotkowski, K., Fuchs, M., Bucher, A. M., Dadras, A., Fischbach, R., Kaltenborn, I. J., Mukhopadhyay, A. 2022; 82: 102596

  View details for DOI 10.1016/j.media.2022.102596

• Disentanglement enables cross-domain hippocampus segmentation IEEE International Symposium on Biomedical Imaging Kalkhof, J., Gonzalez, C., Mukhopadhyay, A. 2022: 1--5

  View details for DOI 10.1109/isbi52829.2022.9761560

• M3d-CAM: A PyTorch library to generate 3D attention maps for medical deep learning Bildverarbeitung für die Medizin Gotkowski, K., Gonzalez, C., Bucher, A. M., Mukhopadhyay, A. 2021: 217--222

  View details for DOI 10.1007/978-3-658-33198-6_52

• Self-supervised out-of-distribution detection for cardiac CMR segmentation Medical Imaging with Deep Learning Gonzalez, C., Mukhopadhyay, A. 2021: 205--218
• Detecting when pre-trained nnu-net models fail silently for covid-19 lung lesion segmentation Medical Image Computing and Computer Assisted Intervention Gonzalez, C., Gotkowski, K., Bucher, A. M., Fischbach, R., Kaltenborn, I., Mukhopadhyay, A. 2021: 304--314

  View details for DOI 10.1007/978-3-030-87234-2_29