Search Results for author: Yijie Zhang

Found 23 papers, 2 papers with code

Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning

no code implementations14 Mar 2024 Xilin Yang, Bijie Bai, Yijie Zhang, Musa Aydin, Sahan Yoruc Selcuk, Zhen Guo, Gregory A. Fishbein, Karine Atlan, William Dean Wallace, Nir Pillar, Aydogan Ozcan

Systemic amyloidosis is a group of diseases characterized by the deposition of misfolded proteins in various organs and tissues, leading to progressive organ dysfunction and failure.

Large Language Model Interaction Simulator for Cold-Start Item Recommendation

no code implementations14 Feb 2024 Feiran Huang, Zhenghang Yang, Junyi Jiang, Yuanchen Bei, Yijie Zhang, Hao Chen

To address this challenge, we propose an LLM Interaction Simulator (LLM-InS) to model users' behavior patterns based on the content aspect.

Collaborative Filtering Language Modelling +2

Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks

no code implementations12 Feb 2024 Yijie Zhang, Yuanchen Bei, Hao Chen, Qijie Shen, Zheng Yuan, Huan Gong, Senzhang Wang, Feiran Huang, Xiao Huang

POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG.

Collaborative Filtering Recommendation Systems

Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network

no code implementations4 Feb 2024 Guangdong Ma, Xilin Yang, Bijie Bai, Jingxi Li, Yuhang Li, Tianyi Gan, Che-Yung Shen, Yijie Zhang, Yuzhu Li, Mona Jarrahi, Aydogan Ozcan

We demonstrated the feasibility of this reconfigurable multiplexed diffractive design by approximating 256 randomly selected permutation matrices using K=4 rotatable diffractive layers.

Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models

no code implementations7 Dec 2023 Yijie Zhang, Zhangyang Gao, Cheng Tan, Stan Z. Li

Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers.

Computational Efficiency

Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering

no code implementations12 Nov 2023 Yijie Zhang, Yuanchen Bei, Shiqi Yang, Hao Chen, Zhiqing Li, Lijia Chen, Feiran Huang

To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering.

Collaborative Filtering Multi-Task Learning +1

If there is no underfitting, there is no Cold Posterior Effect

no code implementations2 Oct 2023 Yijie Zhang, Yi-Shan Wu, Luis A. Ortega, Andrés R. Masegosa

The cold posterior effect (CPE) (Wenzel et al., 2020) in Bayesian deep learning shows that, for posteriors with a temperature $T<1$, the resulting posterior predictive could have better performances than the Bayesian posterior ($T=1$).

Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems

no code implementations22 May 2023 Luzhe Huang, Jianing Li, Xiaofu Ding, Yijie Zhang, Hanlong Chen, Aydogan Ozcan

Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers.

Deblurring Image Deblurring +2

RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design

1 code implementation25 Jan 2023 Cheng Tan, Yijie Zhang, Zhangyang Gao, Bozhen Hu, Siyuan Li, Zicheng Liu, Stan Z. Li

We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure.

Contrastive Learning Protein Design +2

Deep Learning-enabled Virtual Histological Staining of Biological Samples

no code implementations13 Nov 2022 Bijie Bai, Xilin Yang, Yuzhu Li, Yijie Zhang, Nir Pillar, Aydogan Ozcan

Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue.

Virtual impactor-based label-free bio-aerosol detection using holography and deep learning

no code implementations30 Aug 2022 Yi Luo, Yijie Zhang, Tairan Liu, Alan Yu, Yichen Wu, Aydogan Ozcan

To address this need, we present a mobile and cost-effective label-free bio-aerosol sensor that takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides particles larger than ~6 microns to fly through an imaging window.

Virtual stain transfer in histology via cascaded deep neural networks

no code implementations14 Jul 2022 Xilin Yang, Bijie Bai, Yijie Zhang, Yuzhu Li, Kevin De Haan, Tairan Liu, Aydogan Ozcan

Unlike a single neural network structure which only takes one stain type as input to digitally output images of another stain type, C-DNN first uses virtual staining to transform autofluorescence microscopy images into H&E and then performs stain transfer from H&E to the domain of the other stain in a cascaded manner.

Virtual staining of defocused autofluorescence images of unlabeled tissue using deep neural networks

no code implementations6 Jul 2022 Yijie Zhang, Luzhe Huang, Tairan Liu, Keyi Cheng, Kevin De Haan, Yuzhu Li, Bijie Bai, Aydogan Ozcan

Here, we introduce a fast virtual staining framework that can stain defocused autofluorescence images of unlabeled tissue, achieving equivalent performance to virtual staining of in-focus label-free images, also saving significant imaging time by lowering the microscope's autofocusing precision.

Collaborative Inference

Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning

no code implementations30 Jun 2022 Tairan Liu, Yuzhu Li, Hatice Ceylan Koydemir, Yijie Zhang, Ethan Yang, Merve Eryilmaz, Hongda Wang, Jingxi Li, Bijie Bai, Guangdong Ma, Aydogan Ozcan

We also demonstrated that this data-driven plaque assay offers the capability of quantifying the infected area of the cell monolayer, performing automated counting and quantification of PFUs and virus-infected areas over a 10-fold larger dynamic range of virus concentration than standard viral plaque assays.

Specificity Virology

Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning

no code implementations8 Dec 2021 Bijie Bai, Hongda Wang, Yuzhu Li, Kevin De Haan, Francesco Colonnese, Yujie Wan, Jingyi Zuo, Ngan B. Doan, Xiaoran Zhang, Yijie Zhang, Jingxi Li, Wenjie Dong, Morgan Angus Darrow, Elham Kamangar, Han Sung Lee, Yair Rivenson, Aydogan Ozcan

The immunohistochemical (IHC) staining of the human epidermal growth factor receptor 2 (HER2) biomarker is widely practiced in breast tissue analysis, preclinical studies and diagnostic decisions, guiding cancer treatment and investigation of pathogenesis.

Generative Adversarial Network whole slide images

Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

no code implementations4 Mar 2021 Yijie Zhang, Tairan Liu, Manmohan Singh, Yilin Luo, Yair Rivenson, Kirill V. Larin, Aydogan Ozcan

Using 2-fold undersampled spectral data (i. e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in ~6. 73 ms using a desktop computer, removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i. e., 1280 spectral points per A-line).

Image Reconstruction

Deep Coherent Exploration For Continuous Control

no code implementations1 Jan 2021 Yijie Zhang, Herke van Hoof

In policy search methods for reinforcement learning (RL), exploration is often performed by injecting noise either in action space at each step independently or in parameter space over each full trajectory.

Continuous Control Reinforcement Learning (RL)

Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games

1 code implementation14 Nov 2020 Roxana Rădulescu, Timothy Verstraeten, Yijie Zhang, Patrick Mannion, Diederik M. Roijers, Ann Nowé

We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i. e., learning while considering the impact of one's policy when anticipating the opponent's learning step).

Deep learning-based transformation of the H&E stain into special stains

no code implementations20 Aug 2020 Kevin de Haan, Yijie Zhang, Jonathan E. Zuckerman, Tairan Liu, Anthony E. Sisk, Miguel F. P. Diaz, Kuang-Yu Jen, Alexander Nobori, Sofia Liou, Sarah Zhang, Rana Riahi, Yair Rivenson, W. Dean Wallace, Aydogan Ozcan

Based on evaluation by three renal pathologists, followed by adjudication by a fourth renal pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis in several non-neoplastic kidney diseases sampled from 58 unique subjects.

Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

no code implementations20 Jan 2020 Yijie Zhang, Kevin De Haan, Yair Rivenson, Jingxi Li, Apostolos Delis, Aydogan Ozcan

This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a digital staining matrix which represents the desired microscopic map of different stains to be virtually generated at the same tissue section.

A utility-based analysis of equilibria in multi-objective normal form games

no code implementations17 Jan 2020 Roxana Rădulescu, Patrick Mannion, Yijie Zhang, Diederik M. Roijers, Ann Nowé

In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions.

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