Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Periodic graph transformers for crystal material property prediction
We consider representation learning on periodic graphs encoding crystal materials. Different
from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a …
from regular graphs, periodic graphs consist of a minimum unit cell repeating itself on a …
Digital staining in optical microscopy using deep learning-a review
Until recently, conventional biochemical staining had the undisputed status as well-
established benchmark for most biomedical problems related to clinical diagnostics …
established benchmark for most biomedical problems related to clinical diagnostics …
RTNet: relation transformer network for diabetic retinopathy multi-lesion segmentation
Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting
ophthalmologists in diagnosis. Although many researches have been conducted on this …
ophthalmologists in diagnosis. Although many researches have been conducted on this …
Global transformer and dual local attention network via deep-shallow hierarchical feature fusion for retinal vessel segmentation
Clinically, retinal vessel segmentation is a significant step in the diagnosis of fundus
diseases. However, recent methods generally neglect the difference of semantic information …
diseases. However, recent methods generally neglect the difference of semantic information …
Noise2same: Optimizing a self-supervised bound for image denoising
Self-supervised frameworks that learn denoising models with merely individual noisy
images have shown strong capability and promising performance in various image …
images have shown strong capability and promising performance in various image …
[HTML][HTML] Self-supervised learning of hologram reconstruction using physics consistency
Existing applications of deep learning in computational imaging and microscopy mostly
depend on supervised learning, requiring large-scale, diverse and labelled training data …
depend on supervised learning, requiring large-scale, diverse and labelled training data …
Label-free prediction of cell painting from brightfield images
Cell Painting is a high-content image-based assay applied in drug discovery to predict
bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic …
bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic …
Hierarchical feature aggregation based on transformer for image-text matching
In order to carry out more accurate retrieval across image-text modalities, some scholars use
fine-grained feature to align image and text. Most of them directly use attention mechanism …
fine-grained feature to align image and text. Most of them directly use attention mechanism …
Deep learning of high-order interactions for protein interface prediction
Protein interactions are important in a broad range of biological processes. Traditionally,
computational methods have been developed to automatically predict protein interface from …
computational methods have been developed to automatically predict protein interface from …