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Ke Zhang

Alumni

Publications

Bering: joint cell segmentation and annotation for spatial transcriptomics with transferred graph embeddings
Kang Jin
Zuobai Zhang
Ke Zhang
Francesca Viggiani
Claire Callahan
Jian Tang
Bruce J. Aronow
Jian Shu
Single-cell spatial transcriptomics such as in-situ hybridization or sequencing technologies can provide subcellular resolution that enables… (voir plus) the identification of individual cell identities, locations, and a deep understanding of subcellular mechanisms. However, accurate segmentation and annotation that allows individual cell boundaries to be determined remains a major challenge that limits all the above and downstream insights. Current machine learning methods heavily rely on nuclei or cell body staining, resulting in the significant loss of both transcriptome depth and the limited ability to learn latent representations of spatial colocalization relationships. Here, we propose Bering, a graph deep learning model that leverages transcript colocalization relationships for joint noise-aware cell segmentation and molecular annotation in 2D and 3D spatial transcriptomics data. Graph embeddings for the cell annotation are transferred as a component of multi-modal input for cell segmentation, which is employed to enrich gene relationships throughout the process. To evaluate performance, we benchmarked Bering with state-of-the-art methods and observed significant improvement in cell segmentation accuracies and numbers of detected transcripts across various spatial technologies and tissues. To streamline segmentation processes, we constructed expansive pre-trained models, which yield high segmentation accuracy in new data through transfer learning and self-distillation, demonstrating the generalizability of Bering.
2025-07-17
Nature Communications (publié)
doi.org
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
Jonathan Wilder Lavington
Ke Zhang
Vasileios Lioutas
Matthew Niedoba
Yunpeng Liu
Dylan Green
Saeid Naderiparizi
Xiaoxuan Liang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank N. Wood
2024-05-06
ArXiv (prépublication)
doi.org
arxiv.org
Semantically Consistent Video Inpainting with Conditional Diffusion Models
Dylan Green
William Harvey
Saeid Naderiparizi
Matthew Niedoba
Yunpeng Liu
Xiaoxuan Liang
Jonathan Wilder Lavington
Ke Zhang
Vasileios Lioutas
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank N. Wood
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions… (voir plus) by propagating visual information across frames. While such approaches have led to significant progress on standard benchmarks, they struggle with tasks that require the synthesis of novel content that is not present in other frames. In this paper we reframe video inpainting as a conditional generative modeling problem and present a framework for solving such problems with conditional video diffusion models. We highlight the advantages of using a generative approach for this task, showing that our method is capable of generating diverse, high-quality inpaintings and synthesizing new content that is spatially, temporally, and semantically consistent with the provided context.
2024-04-29
ArXiv (prépublication)
doi.org
openreview.net
Nearest Neighbour Score Estimators for Diffusion Generative Models
Matthew Niedoba
Dylan Green
Saeid Naderiparizi
Vasileios Lioutas
Jonathan Wilder Lavington
Xiaoxuan Liang
Yunpeng Liu
Ke Zhang
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank N. Wood
Score function estimation is the cornerstone of both training and sampling from diffusion generative models. Despite this fact, the most com… (voir plus)monly used estimators are either biased neural network approximations or high variance Monte Carlo estimators based on the conditional score. We introduce a novel nearest neighbour score function estimator which utilizes multiple samples from the training set to dramatically decrease estimator variance. We leverage our low variance estimator in two compelling applications. Training consistency models with our estimator, we report a significant increase in both convergence speed and sample quality. In diffusion models, we show that our estimator can replace a learned network for probability-flow ODE integration, opening promising new avenues of future research. Code will be released upon paper acceptance.
2023-12-31
ICML (publié)
doi.org
proceedings.mlr.press