3 citations · 7 across the 8 of their papers we have counts for
9 papers
BIABench: Evaluating AI agents on real-world bioimage analysis tasks
Zixuan Pan, Davide Panzeri, Lukas Johanns +5
Artificial-intelligence (AI) agents hold promise for automating bioimage analysis, yet no benchmark evaluates whether they can carry out real-world analyses end to end. Such analys…
Agentic-J: An AI Agent for Biological Microscopy Image Analysis
Lukas Johanns, Marilin Moor, Davide Panzeri +9
Biological image analysis increasingly demands integration across heterogeneous tools, programming environments, and domain knowledge that few researchers can command simultaneousl…
SGI: Structured 2D Gaussians for Efficient and Compact Large Image Representation
Zixuan Pan, Kaiyuan Tang, Jun Xia +5
2D Gaussian Splatting has emerged as a novel image representation technique that can support efficient rendering on low-end devices. However, scaling to high-resolution images requ…
H-CNN-ViT: A Hierarchical Gated Attention Multi-Branch Model for Bladder Cancer Recurrence Prediction
Xueyang Li, Zongren Wang, Yuliang Zhang +6
Bladder cancer is one of the most prevalent malignancies worldwide, with a recurrence rate of up to 78%, necessitating accurate post-operative monitoring for effective patient mana…
Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspective
Zixuan Pan, Jun Xia, Zheyu Yan +7
Reconstruction-based methods, particularly those leveraging autoencoders, have been widely adopted for anomaly detection task in brain MRI. Unlike most existing works try to improv…
TSB: Tiny Shared Block for Efficient DNN Deployment on NVCIM Accelerators
Yifan Qin, Zheyu Yan, Zixuan Pan +3
Compute-in-memory (CIM) accelerators using non-volatile memory (NVM) devices offer promising solutions for energy-efficient and low-latency Deep Neural Network (DNN) inference exec…
Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge Devices
Ruiyang Qin, Dancheng Liu, Chenhui Xu +9
The scaling laws have become the de facto guidelines for designing large language models (LLMs), but they were studied under the assumption of unlimited computing resources for bot…
Efficient Vision-Language Pre-training by Cluster Masking
Zihao Wei, Zixuan Pan, Andrew Owens
We propose a simple strategy for masking image patches during visual-language contrastive learning that improves the quality of the learned representations and the training speed.…
NeFT: Negative Feedback Training to Improve Robustness of Compute-In-Memory DNN Accelerators
Yifan Qin, Zheyu Yan, Dailin Gan +5
Compute-in-memory accelerators built upon non-volatile memory devices excel in energy efficiency and latency when performing deep neural network (DNN) inference, thanks to their in…