Chixiang Lu
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View article: EM Generalist: A physics-driven diffusion foundation model for electron microscopy
EM Generalist: A physics-driven diffusion foundation model for electron microscopy Open
Electron microscopy (EM) is an indispensable tool for visualizing biological structures at the nanoscale. However, high-fidelity EM imaging is often time-consuming, and 3D isotropic volume EM (vEM) remains impractical for large-scale analy…
View article: BOOST: a robust ten-fold expansion method on hour-scale
BOOST: a robust ten-fold expansion method on hour-scale Open
Expansion microscopy enhances the microscopy resolution by physically expanding biological specimens and improves the visualization of structural and molecular details. Numerous expansion microscopy techniques and labeling methods have bee…
View article: Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy
Diffusion-based deep learning method for augmenting ultrastructural imaging and volume electron microscopy Open
View article: EMDiffuse: a diffusion-based deep learning method augmenting ultrastructural imaging and volume electron microscopy
EMDiffuse: a diffusion-based deep learning method augmenting ultrastructural imaging and volume electron microscopy Open
Electron microscopy (EM) revolutionized the way to visualize cellular ultrastructure. Volume EM (vEM) has further broadened its three-dimensional nanoscale imaging capacity. However, intrinsic trade-offs between imaging speed and quality o…
View article: GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis Open
Self-supervised representation learning has been extremely successful in medical image analysis, as it requires no human annotations to provide transferable representations for downstream tasks. Recent self-supervised learning methods are …
View article: PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis Open
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the prese…
View article: Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts
Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contexts Open
Preserving maximal information is one of principles of designing self-supervised learning methodologies. To reach this goal, contrastive learning adopts an implicit way which is contrasting image pairs. However, we believe it is not fully …
View article: ConvNets vs. Transformers: Whose Visual Representations are More Transferable?
ConvNets vs. Transformers: Whose Visual Representations are More Transferable? Open
Vision transformers have attracted much attention from computer vision researchers as they are not restricted to the spatial inductive bias of ConvNets. However, although Transformer-based backbones have achieved much progress on ImageNet …
View article: Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation
Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation Open
Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the abi…