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Found 10 papers, 1 papers with code
Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey
This survey serves as a roadmap for researchers, offering practical guidance for future exploration in the field of INRs.
Biophysics Informed Pathological Regularisation for Brain Tumour Segmentation
Recent advances in deep learning have significantly improved brain tumour segmentation techniques; however, the results still lack confidence and robustness as they solely consider image data without biophysical priors or pathological information.
Single-Shot Plug-and-Play Methods for Inverse Problems
In this work, we introduce Single-Shot PnP methods (SS-PnP), shifting the focus to solving inverse problems with minimal data.
The Missing U for Efficient Diffusion Models
Diffusion Probabilistic Models stand as a critical tool in generative modelling, enabling the generation of complex data distributions.
Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease Diagnosis using Brain Images
Structural MRI and PET imaging play an important role in the diagnosis of Alzheimer's disease (AD), showing the morphological changes and glucose metabolism changes in the brain respectively.
Multi-modal learning for predicting the genotype of glioma
In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks.
Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading
In this proposed scheme, we design a normalized modality contrastive loss (NMC-loss), which could promote to disentangle multi-modality complementary representation of FFPE and frozen sections from the same patient.
Metastatic Cancer Image Classification Based On Deep Learning Method
Using histopathological images to automatically classify cancer is a difficult task for accurately detecting cancer, especially to identify metastatic cancer in small image patches obtained from larger digital pathology scans.
Residual Recurrent CRNN for End-to-End Optical Music Recognition on Monophonic Scores
One of the challenges of the Optical Music Recognition task is to transcript the symbols of the camera-captured images into digital music notations.
Dual Encoder Fusion U-Net (DEFU-Net) for Cross-manufacturer Chest X-ray Segmentation
In order to increase the width of network and enrich representation of features, the inception blocks with dilation are adopted.
