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Found 20 papers, 10 papers with code
DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model
Specifically, DiffImpute is trained on complete tabular datasets, ensuring that it can produce credible imputations for missing entries without undermining the authenticity of the existing data.
FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images
Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly prompted.
SAM-Lightening: A Lightweight Segment Anything Model with Dilated Flash Attention to Achieve 30 times Acceleration
Moreover, it takes only 244MB memory, which is 3. 5\% of the vanilla SAM.
A Survey for Foundation Models in Autonomous Driving
The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD).
Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey
The advent of foundation models, which are pre-trained on vast datasets, has ushered in a new era of computer vision, characterized by their robustness and remarkable zero-shot generalization capabilities.
SAM-Med3D
These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information.
Tackling Heterogeneity in Medical Federated learning via Vision Transformers
Optimization-based regularization methods have been effective in addressing the challenges posed by data heterogeneity in medical federated learning, particularly in improving the performance of underrepresented clients.
Artifact Restoration in Histology Images with Diffusion Probabilistic Models
Histological whole slide images (WSIs) can be usually compromised by artifacts, such as tissue folding and bubbles, which will increase the examination difficulty for both pathologists and Computer-Aided Diagnosis (CAD) systems.
TransNuSeg: A Lightweight Multi-Task Transformer for Nuclei Segmentation
In conclusion, TransNuSeg confirms the strength of Transformer in the context of nuclei segmentation, which thus can serve as an efficient solution for real clinical practice.
Graph Denoising Diffusion for Inverse Protein Folding
In contrast, diffusion probabilistic models, as an emerging genre of generative approaches, offer the potential to generate a diverse set of sequence candidates for determined protein backbones.
SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency
Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction.
MoViT: Memorizing Vision Transformers for Medical Image Analysis
We evaluate our method on a public histology image dataset and an in-house MRI dataset, demonstrating that MoViT applied to varied medical image analysis tasks, can outperform vanilla transformer models across varied data regimes, especially in cases where only a small amount of annotated data is available.
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism
However, with heterogeneous syndromes and distinct phenotypes, DL models trained with CTs from one data center fail to generalize on images from another center.
RandStainNA: Learning Stain-Agnostic Features from Histology Slides by Bridging Stain Augmentation and Normalization
To address the problems, we unify SN and SA with a novel RandStainNA scheme, which constrains variable stain styles in a practicable range to train a stain agnostic deep learning model.
Mixed Sample Augmentation for Online Distillation
Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful data augmentation strategy to generalize convolutional neural networks.
How GNNs Facilitate CNNs in Mining Geometric Information from Large-Scale Medical Images
Existing solutions heavily rely on convolutional neural networks (CNNs) for global pixel-level analysis, leaving the underlying local geometric structure such as the interaction between cells in the tumor microenvironment unexplored.
CD$^2$-pFed: Cyclic Distillation-guided Channel Decoupling for Model Personalization in Federated Learning
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.
Self-Distillation from the Last Mini-Batch for Consistency Regularization
Afterwards, the former half mini-batch distills on-the-fly soft targets generated in the previous iteration.
CD2-pFed: Cyclic Distillation-Guided Channel Decoupling for Model Personalization in Federated Learning
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model.
A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image
The large-scale OCTA dataset is available at https://doi. org/10. 5281/zenodo. 5111975, https://doi. org/10. 5281/zenodo. 5111972.
