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Found 29 papers, 6 papers with code
Virtual Staining of Label-Free Tissue in Imaging Mass Spectrometry
Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research.
Super-resolved virtual staining of label-free tissue using diffusion models
Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples.
On Cold Posteriors of Probabilistic Neural Networks: Understanding the Cold Posterior Effect and A New Way to Learn Cold Posteriors with Tight Generalization Guarantees
In Bayesian inference, when $T < 1$ (``cold'' posteriors), the likelihood is up-weighted, resulting in a sharper posterior distribution.
Label-free evaluation of lung and heart transplant biopsies using virtual staining
Here, we present a panel of virtual staining neural networks for lung and heart transplant biopsies, which digitally convert autofluorescence microscopic images of label-free tissue sections into their brightfield histologically stained counterparts, bypassing the traditional histochemical staining process.
Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
While PAC-Bayes allows construction of data-informed priors, the final confidence intervals depend only on the number of points that were not used for the construction of the prior, whereas confidence information in the prior, which is related to the number of points used to construct the prior, is lost.
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
Human epidermal growth factor receptor 2 (HER2) is a critical protein in cancer cell growth that signifies the aggressiveness of breast cancer (BC) and helps predict its prognosis.
Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning
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.
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
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.
Multiplexed all-optical permutation operations using a reconfigurable diffractive optical network
We demonstrated the feasibility of this reconfigurable multiplexed diffractive design by approximating 256 randomly selected permutation matrices using K=4 rotatable diffractive layers.
Realigning Confidence with Temporal Saliency Information for Point-Level Weakly-Supervised Temporal Action Localization
In CSL we design a novel center label generated by the point annotations for predicting aligned center scores.
Weakly-supervised Temporal Action Localization
Weakly Supervised Temporal Action Localization
Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models
Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers.
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering.
If there is no underfitting, there is no Cold Posterior Effect
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
Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers.
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure.
Deep Learning-enabled Virtual Histological Staining of Biological Samples
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
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
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
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.
Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning
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.
Label-free virtual HER2 immunohistochemical staining of breast tissue using deep learning
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.
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
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).
Deep Coherent Exploration For Continuous Control
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.
On the Inconsistency of Bayesian Inference for Misspecified Neural Networks
Grunwald and Van Ommen (2017) show that Bayesian inference for linear regression can be inconsistent under model misspecification.
Opponent Learning Awareness and Modelling in Multi-Objective Normal Form Games
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
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
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
In multi-objective multi-agent systems (MOMAS), agents explicitly consider the possible tradeoffs between conflicting objective functions.
Relation Mention Extraction from Noisy Data with Hierarchical Reinforcement Learning
Experimental results show that our model is effective to extract relation mentions from noisy data.
