Characterizing ferrous versus ferric iron in Alzheimer's disease using X-ray fluorescence imaging and XANES spectroscopy Dean Tran, Marios Georgiadis, Philip DiGiacomo, Jeff Nirschl, Inma Cobos, Jarrett Rosenberg, Nicholas Edwards, Sharon Bone, Samuel Webb, Michael Zeineh Journal of Alzheimer S Disease, 2026 Background The accumulation of iron, such as ferrous Fe 2+ , in the Alzheimer's disease (AD) brain may contribute to neurodegeneration by driving oxidative stress. While elevated iron in AD has been shown, the oxidation state of iron and its regional distribution in AD, particularly in the hippocampus, is unclear. Objective To characterize the oxidation state and spatial distribution of iron in the hippocampus of AD and control brains, and to assess the effect of tissue thawing on ferrous iron measurements. Methods We utilized X-ray fluorescence imaging and X-ray absorption near edge structure spectroscopy to localize and analyze iron deposition in fresh-frozen human hippocampal specimens stratified by AD disease stage. To assess the effect of thawing on iron oxidation, we used a cryo-chamber to keep three specimens frozen while their respective deposits were being scanned. These specimens were then allowed to thaw and their same deposits were rescanned for comparison. Results Compared to control brains, AD specimens exhibited elevated levels of ferrous iron (Fe 2 + ) in the cornu ammonis 1 (CA1)-subiculum subfields—regions known to degenerate early in AD. We also measured a decrease in Fe 2+ levels in AD and control specimens scanned after being thawed. Conclusions Our findings support the association between elevated Fe 2+ and AD, consistent with existing hypotheses linking redox-active iron to oxidative stress and neuroinflammation. The observed reduction in Fe 2+ levels following thawing suggests that studies using thawed brain samples may underestimate Fe 2+ levels.
AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer Zhe Li, Yuchen Li, Jinxi Xiang, Xiyue Wang, Sen Yang, Xiaoming Zhang, Feyisope Eweje, Yijiang Chen, Xiangde Luo, Yuanyuan Li, Jonathan Mulholland, Colin Bergstrom, Ted Kim, Francesca Maria Olguin, Sierra Willens, Jeffrey J. Nirschl, Robert West, Joel Neal, Maximilian Diehn, Ruijiang Li Nature Medicine, 2026 Spatial proteomics enables high-resolution mapping of protein expression and can transform our understanding of biology and disease. However, major challenges remain for clinical translation, including cost, complexity and scalability. Here we present H&E to protein expression (HEX), an AI model designed to computationally generate spatial proteomics profiles from standard histopathology slides. Trained and validated on 819,000 histopathology image tiles with matched protein expression from 382 tumor samples, HEX accurately predicts the expression of 40 biomarkers encompassing immune, structural and functional programs. HEX demonstrates substantial performance gains over alternative methods for protein expression prediction from H&E images. We develop a multimodal data integration approach that combines the original H&E image and AI-derived virtual spatial proteomics to enhance outcome prediction. Applied to six independent non-small-cell lung cancer cohorts totaling 2,298 patients, HEX-enabled multimodal integration improved prognostic accuracy by 22% and immunotherapy response prediction by 24–39% compared with conventional clinicopathological and molecular biomarkers. Biological interpretation revealed spatially organized tumor–immune niches predictive of therapeutic response, including the co-localization of T helper cells and cytotoxic T cells in responders, and immunosuppressive tumor-associated macrophage and neutrophil aggregates in non-responders. HEX provides a low-cost and scalable approach to study spatial biology and enables the discovery and clinical translation of interpretable biomarkers for precision medicine.
Micron-resolution fiber mapping in histology independent of sample preparation Marios Georgiadis, Franca auf der Heiden, Hamed Abbasi, Loes Ettema, Jeffrey Nirschl, Hossein Moein Taghavi, Moe Wakatsuki, Andy Liu, William Hai Dang Ho, Mackenzie Carlson, Michail Doukas, Sjors A. Koppes, Stijn Keereweer, Raymond A. Sobel, Kawin Setsompop, Congyu Liao, Katrin Amunts, Markus Axer, Michael Zeineh, Miriam Menzel Nature Communications, 2025 Mapping the brain’s fiber network is crucial for understanding its function and malfunction, but resolving nerve trajectories over large fields of view is challenging. Here, we show that computational scattered light imaging (ComSLI) can map fiber networks in histology independent of sample preparation, also in formalin-fixed paraffin-embedded (FFPE) tissues including whole human brain sections. We showcase this method in new and archived, animal and human brain sections, for different sample preparations (in paraffin, deparaffinized, various stains, unstained fresh-frozen). We convert microscopic orientations to microstructure-informed fiber orientation distributions (μFODs). Adapting tractography tools from diffusion magnetic resonance imaging (dMRI), we trace axonal trajectories revealing white and gray matter connectivity. These allow us to identify altered microstructure or deficient tracts in demyelinating or neurodegenerating pathology, and to show key advantages over dMRI, polarization microscopy, and structure tensor analysis. Finally, we map fibers in non-brain tissues, including muscle, bone, and blood vessels, unveiling the tissue’s function. Our cost-effective, versatile approach enables micron-resolution studies of intricate fiber networks across tissues, species, diseases, and sample preparations, offering new dimensions to neuroscientific and biomedical research.
Precise MRI-histology coregistration of paraffin-embedded tissue with blockface imaging Yixin Wang, William Ho, Istvan N. Huszar, Phillip DiGiacomo, Hossein Moein Taghavi, Lee Tao, Matthew Choi, Nhu Nguyen, Samantha Leventis, David B. Camarillo, Philipp Schlömer, Markus Axer, Wei Shao, Mirabela Rusu, Inma Cobos, Jeff Nirschl, Marios Georgiadis, Michael Zeineh Imaging Neuroscience, 2025 Magnetic resonance imaging (MRI) provides 3D spatial information on tissue, yet it lacks at the molecular level. In contrast, histology provides cellular and molecular information, but it lacks the 3D spatial context and direct in vivo translation. Coregistering the two is key for the 3D embedding of histological details, validating pathological MRI findings, and identifying quantitative imaging biomarkers of neurodegenerative diseases. However, coregistration is challenging due to non-linear distortions of the tissue from histological processing and sectioning leading to microscopic and macroscopic nonlinear 3D deformations between specimen MRI and stained histology sections. To address this, we developed a novel pipeline, named Brewster’s Blockface Quantification (BBQ), integrating robust optical approaches with innovative 2D and 3D registration algorithms to achieve precise volumetric alignment of specimen MRI data with histological images. On a variety of brain tissue specimens from distinct anatomical regions and across multiple species, our methodology generated blockface volumes with minimal distortion and artifacts. Using these blockface volumes as an intermediary, we achieve a precise alignment between MRI and histology slides, yielding registration results with an overlapping Dice score of ~90% for whole tissue alignment between MRI and blockface volumes, and >95% for 2D MRI-histology registration. This correlative MRI-histology pipeline with robust 2D and 3D coregistration methods promises to enhance our understanding of neurodegenerative diseases and aid the development of MRI-based disease biomarkers.
BRAF/MEK inhibition induces cell state transitions boosting immune checkpoint sensitivity in BRAFV600E-mutant glioma Yao Lulu Xing, Dena Panovska, Jong-Whi Park, Stefan Grossauer, Katharina Koeck, Brandon Bui, Emon Nasajpour, Jeffrey J. Nirschl, Zhi-Ping Feng, Pierre Cheung, Pardes Habib, Ruolun Wei, Jie Wang, Wes Thomason, Michelle Monje, Joanne Xiu, Alexander Beck, Katharina J. Weber, Patrick N. Harter, Michael Lim, Kelly B. Mahaney, Laura M. Prolo, Gerald A. Grant, Xuhuai Ji, Kyle M. Walsh, Jean M. Mulcahy Levy, Dolores Hambardzumyan, Claudia K. Petritsch Cell Reports Medicine, 2025 Xing et al. report that combined BRAF and MEK inhibition (BRAFi+MEKi) in BRAFV600E-mutant high-grade glioma shifts tumor cell states and upregulates PD-L1, via Galectin-3 secretion, contributing to T cell suppression. Concurrent but not sequential immune checkpoint inhibition in mice overcomes these tumor-intrinsic adaptations, highlighting its translational promise for glioma therapy.
A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies Zhi Huang, Eric Yang, Jeanne Shen, Dita Gratzinger, Frederick Eyerer, Brooke Liang, Jeffrey Nirschl, David Bingham, Alex M. Dussaq, Christian Kunder, Rebecca Rojansky, Aubre Gilbert, Alexandra L. Chang-Graham, Brooke E. Howitt, Ying Liu, Emily E. Ryan, Troy B. Tenney, Xiaoming Zhang, Ann Folkins, Edward J. Fox, Kathleen S. Montine, Thomas J. Montine, James Zou Nature Biomedical Engineering, 2025
The impact of arteriolosclerosis on cognitive impairment in decedents without severe dementia from the National Alzheimer's Coordinating Center Cellas A. Hayes, Christina B. Young, Carla Abdelnour, Alexis Reeves, Michelle C. Odden, Jeffrey Nirschl, Paul K. Crane, Kathleen L. Poston, Elizabeth C. Mormino, Kyan Younes Alzheimer S and Dementia, 2025 INTRODUCTIONAlzheimer's disease neuropathologic change (ADNC), Lewy body disease (LBD), and vascular neuropathologies occur together. Previous studies have been limited by a large majority of participants with severe dementia or advanced stages of pathologies, which limits the detectability of cognitive effects from vascular neuropathologies.METHODSUsing neuropathology data from the National Alzheimer's Coordinating Center, we examined the association of vascular neuropathologies with cognitive scores in participants without severe dementia (N = 1526) using multivariable linear regression.RESULTSControlling for age, sex, education, LBD, and ADNC, arteriolosclerosis was associated with lower memory (β = −0.16 ± 0.06, p < 0.001), executive function (β = −0.25 ± 0.05, p < 0.001), and language scores (β = −0.20 ± 0.05, p < 0.001). The effects of arteriolosclerosis remained when controlling for vascular risk factors.DISCUSSIONVascular neuropathologies exhibit distinct relationships with cognition. Arteriolosclerosis is an independent contributor to cognition. Further research should be conducted on whether arteriolosclerosis can serve as a surrogate marker for cognitive decline in early disease stages.Highlights In individuals who do not have severe dementia, vascular neuropathologies are common, and the combination of pathologies is heterogeneous in a convenience sample from the Alzheimer's Disease Research Center that reported all the neuropathology data elements for this investigation. Arteriolosclerosis is associated with several cognitive domain scores, including memory, executive function, and language when controlling for the effects of Alzheimer's disease neuropathologic change and Lewy body disease. These results reinforce the importance of vascular pathology for cognition among people along the Alzheimer's disease spectrum.
A vision–language foundation model for precision oncology Jinxi Xiang, Xiyue Wang, Xiaoming Zhang, Yinghua Xi, Feyisope Eweje, Yijiang Chen, Yuchen Li, Colin Bergstrom, Matthew Gopaulchan, Ted Kim, Kun-Hsing Yu, Sierra Willens, Francesca Maria Olguin, Jeffrey J. Nirschl, Joel Neal, Maximilian Diehn, Sen Yang, Ruijiang Li Nature, 2025
The Impact of Image Resolution on Biomedical Multimodal Large Language Models Proceedings of Machine Learning Research, 2025
CellFlux: Simulating Cellular Morphology Changes via Flow Matching Proceedings of Machine Learning Research, 2025
MicroVQA: A Multimodal Reasoning Benchmark for Microscopy-Based Scientific Research James Burgess, Jeffrey J Nirschl, Laura Bravo-Sánchez, Alejandro Lozano, Sanket Rajan Gupte, Jesus G. Galaz-Montoya, Yuhui Zhang, Yuchang Su, Disha Bhowmik, Zachary Coman, Sarina M. Hasan, Alexandra Johannesson, William D. Leineweber, Malvika G Nair, Ridhi Yarlagadda, Connor Zuraski, Wah Chiu, Sarah Cohen, Jan N. Hansen, Manuel D Leonetti, Chad Liu, Emma Lundberg, Serena Yeung-Levy Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2025
TDP43 pathology in chronic traumatic encephalopathy retinas Ragini Phansalkar, Vanessa S. Goodwill, Jeffrey J. Nirschl, Chiara De Lillo, Jihee Choi, Elizabeth Spurlock, David G. Coughlin, Donald Pizzo, Christina J. Sigurdson, Annie Hiniker, Victor E. Alvarez, Ann C. Mckee, Jonathan H. Lin Acta Neuropathologica Communications, 2023
Expanded analysis of high-grade astrocytoma with piloid features identifies an epigenetically and clinically distinct subtype associated with neurofibromatosis type 1 Patrick J. Cimino, Courtney Ketchum, Rust Turakulov, Omkar Singh, Zied Abdullaev, Caterina Giannini, Peter Pytel, Giselle Yvette Lopez, Howard Colman, MacLean P. Nasrallah, Mariarita Santi, Igor Lima Fernandes, Jeff Nirschl, Sonika Dahiya, Stewart Neill, David Solomon, Eilis Perez, David Capper, Haresh Mani, Dario Caccamo, Matthew Ball, Michael Badruddoja, Rati Chkheidze, Sandra Camelo-Piragua, Joseph Fullmer, Sanda Alexandrescu, Gabrielle Yeaney, Charles Eberhart, Maria Martinez-Lage, Jie Chen, Leor Zach, B. K. Kleinschmidt-DeMasters, Marco Hefti, Maria-Beatriz Lopes, Nicholas Nuechterlein, Craig Horbinski, Fausto J. Rodriguez, Martha Quezado, Drew Pratt, Kenneth Aldape Acta Neuropathologica, 2023
Biological data annotation via a human-augmenting AI-based labeling system Douwe van der Wal, Iny Jhun, Israa Laklouk, Jeff Nirschl, Lara Richer, Rebecca Rojansky, Talent Theparee, Joshua Wheeler, Jörg Sander, Felix Feng, Osama Mohamad, Silvio Savarese, Richard Socher, Andre Esteva Npj Digital Medicine, 2021
Actin cables and comet tails organize mitochondrial networks in mitosis Andrew S. Moore, Stephen M. Coscia, Cory L. Simpson, Fabian E. Ortega, Eric C. Wait, John M. Heddleston, Jeffrey J. Nirschl, Christopher J. Obara, Pedro Guedes-Dias, C. Alexander Boecker, Teng-Leong Chew, Julie A. Theriot, Jennifer Lippincott-Schwartz, Erika L. F. Holzbaur Nature, 2021
The development and convergence of co-pathologies in Alzheimer's disease John L Robinson, Hayley Richardson, Sharon X Xie, EunRan Suh, Vivianna M Van Deerlin, Brian Alfaro, Nicholas Loh, Matias Porras-Paniagua, Jeffrey J Nirschl, David Wolk, Virginia M -Y Lee, Edward B Lee, John Q Trojanowski Brain, 2021
In vitro amplification of pathogenic tau conserves disease-specific bioactive characteristics Hong Xu, Mia O’Reilly, Garrett S. Gibbons, Lakshmi Changolkar, Jennifer D. McBride, Dawn M. Riddle, Bin Zhang, Anna Stieber, Jeffrey Nirschl, Soo-Jung Kim, Kevt-her Hoxha, Kurt R. Brunden, Gerard D. Schellenberg, John Q. Trojanowski, Virginia M.-Y. Lee Acta Neuropathologica, 2021
iSight: Towards expert-AI co-assessment for improved immunohistochemistry staining interpretation JS Leiby, J Yao, P Lu, G Hu, A Davidian, S Koga, O Leung, P Patel, ... arXiv preprint arXiv:2602.04063 , 2026 2026
Uncertainty-Aware Image Classification In Biomedical Imaging Using Spectral-normalized Neural Gaussian Processes U Meleti, JJ Nirschl arXiv preprint arXiv:2602.02370 , 2026 2026
AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer Z Li, Y Li, J Xiang, X Wang, S Yang, X Zhang, F Eweje, Y Chen, X Luo, ... Nature Medicine, 1-14 , 2026 2026 Citations: 25
CellFluxV2: An Image Generative Foundation Model for Virtual Cell Modeling Y Zhang, Y Su, Z Wefers, S Su, H Li, T Li, C Wang, J Burgess, A Lozano, ... bioRxiv, 2026.01. 19.696785 , 2026 2026 Citations: 1
Alzheimer's Imaging Consortium. M Georgiadis, F Auf der Heiden, J Nirschl, A Liu, HM Taghavi, K Amunts, ... Alzheimer's & dementia: the journal of the Alzheimer's Association 21 … , 2025 2025
Basic Science and Pathogenesis. M Georgiadis, F Auf der Heiden, J Nirschl, A Liu, HM Taghavi, K Amunts, ... Alzheimer's & Dementia: the Journal of the Alzheimer's Association 21 … , 2025 2025
Mapping neuronal trajectories in neurodegeneration independent of sample preparation M Georgiadis, F auf der Heiden, J Nirschl, A Liu, HM Taghavi, K Amunts, ... Alzheimer's & Dementia 21, e106440 , 2025 2025
Micron-resolution fiber mapping in histology independent of sample preparation M Georgiadis, F Auf Der Heiden, H Abbasi, L Ettema, J Nirschl, ... Nature Communications 16 (1), 9572 , 2025 2025 Citations: 10
TMIC-05. Unraveling transcriptomic and phosphokinomic dynamics of the tumor microenvironment during glioma progression YL Xing, B Bui, K Cordero, E Ngok, C Magtoto, D Panovska, J Nirschl, ... Neuro-Oncology 27 (Supplement_5), v444-v444 , 2025 2025
The Impact of Image Resolution on Biomedical Multimodal Large Language Models L Chen, J Burgess, JJ Nirschl, O Zohar, S Yeung-Levy arXiv preprint arXiv:2510.18304 , 2025 2025 Citations: 1
HGG-05. Dual BRAF and MAPK Inhibition Promotes Glial Differentiation and Immune Evasion via Galectin-3 D Panovska, L Xing, S Grossauer, J Park, E Nasajpour, R Wei, B Bui, ... Neuro-Oncology Pediatrics 1 (Supplement_1), wuaf001. 141 , 2025 2025
BRAF/MEK inhibition induces cell state transitions boosting immune checkpoint sensitivity in BRAFV600E-mutant glioma YL Xing, D Panovska, JW Park, S Grossauer, K Koeck, B Bui, E Nasajpour, ... Cell Reports Medicine 6 (6) , 2025 2025 Citations: 7
Digital pathology–based AI spatial biomarker to predict outcomes for immune checkpoint inhibitors in advanced non-small cell lung cancer. F Eweje, Z Li, Y Li, CP Bergstrom, T Kim, F Olguin, SH Willens, ... Journal of Clinical Oncology 43 (16_suppl), 8569-8569 , 2025 2025 Citations: 1
A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies Z Huang, E Yang, J Shen, D Gratzinger, F Eyerer, B Liang, J Nirschl, ... Nature Biomedical Engineering 9 (4), 455-470 , 2025 2025 Citations: 62
A large-scale vision-language dataset derived from open scientific literature to advance biomedical generalist ai A Lozano, MW Sun, J Burgess, JJ Nirschl, C Polzak, Y Zhang, L Chen, ... arXiv preprint arXiv:2503.22727 , 2025 2025 Citations: 5
143 Artificial Intelligence-enabled Spatial Tumor Microenvironment Profiling Predicts Response to Immunotherapy in Invasive Breast Carcinoma F Eweje, Z Li, K Yuan, F Olguin, C Bergstrom, J Nirschl, R Li Laboratory Investigation 105 (3) , 2025 2025
The impact of arteriolosclerosis on cognitive impairment in decedents without severe dementia from the National Alzheimer's Coordinating Center CA Hayes, CB Young, C Abdelnour, A Reeves, MC Odden, J Nirschl, ... Alzheimer's & Dementia 21 (3), e70059 , 2025 2025 Citations: 13
A vision–language foundation model for precision oncology J Xiang, X Wang, X Zhang, Y Xi, F Eweje, Y Chen, Y Li, C Bergstrom, ... Nature 638 (8051), 769-778 , 2025 2025 Citations: 310
Cellflux: Simulating cellular morphology changes via flow matching Y Zhang, Y Su, C Wang, T Li, Z Wefers, J Nirschl, J Burgess, D Ding, ... arXiv preprint arXiv:2502.09775 , 2025 2025 Citations: 21
Molecular, histologic, and clinical characterization of methylation class pleomorphic xanthoastrocytoma: An analysis of 469 tumors CH Dampier, N Shah, K Galbraith, A Ebrahimi, OLA Neto, Z Abdullaev, ... Neuro-Oncology Advances 7 (1), vdaf089 , 2025 2025 Citations: 4
MOST CITED SCHOLAR PUBLICATIONS
Deep learning for medical image analysis SK Zhou, H Greenspan, D Shen Academic Press , 2023 2023 Citations: 513
A vision–language foundation model for precision oncology J Xiang, X Wang, X Zhang, Y Xi, F Eweje, Y Chen, Y Li, C Bergstrom, ... Nature 638 (8051), 769-778 , 2025 2025 Citations: 310
Amyotrophic lateral sclerosis-linked mutations increase the viscosity of liquid-like TDP-43 RNP granules in neurons PP Gopal, JJ Nirschl, E Klinman, ELF Holzbaur Proceedings of the National Academy of Sciences 114 (12), E2466-E2475 , 2017 2017 Citations: 305
LC3 binding to the scaffolding protein JIP1 regulates processive dynein-driven transport of autophagosomes M Fu, JJ Nirschl, ELF Holzbaur Developmental cell 29 (5), 577-590 , 2014 2014 Citations: 241
α-Tubulin tyrosination and CLIP-170 phosphorylation regulate the initiation of dynein-driven transport in neurons JJ Nirschl, MM Magiera, JE Lazarus, C Janke, ELF Holzbaur Cell reports 14 (11), 2637-2652 , 2016 2016 Citations: 224
The development and convergence of co-pathologies in Alzheimer’s disease JL Robinson, H Richardson, SX Xie, ER Suh, VM Van Deerlin, B Alfaro, ... Brain 144 (3), 953-962 , 2021 2021 Citations: 216
Actin cables and comet tails organize mitochondrial networks in mitosis AS Moore, SM Coscia, CL Simpson, FE Ortega, EC Wait, JM Heddleston, ... Nature 591 (7851), 659-664 , 2021 2021 Citations: 194
Kinesin-3 responds to local microtubule dynamics to target synaptic cargo delivery to the presynapse P Guedes-Dias, JJ Nirschl, N Abreu, MK Tokito, C Janke, MM Magiera, ... Current Biology 29 (2), 268-282. e8 , 2019 2019 Citations: 173
A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue JJ Nirschl, A Janowczyk, EG Peyster, R Frank, KB Margulies, ... PloS one 13 (4), e0192726 , 2018 2018 Citations: 160
The impact of cytoskeletal organization on the local regulation of neuronal transport JJ Nirschl, AE Ghiretti, ELF Holzbaur Nature Reviews Neuroscience 18 (10), 585-597 , 2017 2017 Citations: 116
A pathologist–AI collaboration framework for enhancing diagnostic accuracies and efficiencies Z Huang, E Yang, J Shen, D Gratzinger, F Eyerer, B Liang, J Nirschl, ... Nature Biomedical Engineering 9 (4), 455-470 , 2025 2025 Citations: 62
In vitro amplification of pathogenic tau conserves disease-specific bioactive characteristics H Xu, M O’Reilly, GS Gibbons, L Changolkar, JD McBride, DM Riddle, ... Acta Neuropathologica, 1-23 , 2021 2021 Citations: 58
Biomedica: An open biomedical image-caption archive, dataset, and vision-language models derived from scientific literature A Lozano, MW Sun, J Burgess, L Chen, JJ Nirschl, J Gu, I Lopez, J Aklilu, ... Proceedings of the Computer Vision and Pattern Recognition Conference, 19724 … , 2025 2025 Citations: 43
Histomorphometric classifier to predict cardiac failure from whole-slide hematoxylin and eosin stained images A Madabhushi, JJ Nirschl, A Janowczyk, EG Peyster, MD Feldman, ... US Patent 10,528,848 , 2020 2020 Citations: 42
Automated Quantification of Locomotion, Social Interaction, and Mate Preference in Drosophila Mutants A Iyengar, J Imoehl, A Ueda, J Nirschl, CF Wu Journal of neurogenetics 26 (3-4), 306-316 , 2012 2012 Citations: 42
Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles J Burgess, JJ Nirschl, MC Zanellati, A Lozano, S Cohen, S Yeung-Levy Nature Communications 15 (1), 1022 , 2024 2024 Citations: 40
Deep learning tissue segmentation in cardiac histopathology images JJ Nirschl, A Janowczyk, EG Peyster, R Frank, KB Margulies, ... Deep learning for medical image analysis, 179-195 , 2017 2017 Citations: 32
Microvqa: A multimodal reasoning benchmark for microscopy-based scientific research J Burgess, JJ Nirschl, L Bravo-Sánchez, A Lozano, SR Gupte, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern … , 2025 2025 Citations: 30
AI-enabled virtual spatial proteomics from histopathology for interpretable biomarker discovery in lung cancer Z Li, Y Li, J Xiang, X Wang, S Yang, X Zhang, F Eweje, Y Chen, X Luo, ... Nature Medicine, 1-14 , 2026 2026 Citations: 25
Cellflux: Simulating cellular morphology changes via flow matching Y Zhang, Y Su, C Wang, T Li, Z Wefers, J Nirschl, J Burgess, D Ding, ... arXiv preprint arXiv:2502.09775 , 2025 2025 Citations: 21
