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Found 12 papers, 6 papers with code
Synthetic Privileged Information Enhances Medical Image Representation Learning
Multimodal self-supervised representation learning has consistently proven to be a highly effective method in medical image analysis, offering strong task performance and producing biologically informed insights.
Training-Free Pretrained Model Merging
Furthermore, the visualization of the merged model within the multi-task loss landscape reveals that MuDSC enables the merged model to reside in the overlapping segment, featuring a unified lower loss for each task.
TriDeNT: Triple Deep Network Training for Privileged Knowledge Distillation in Histopathology
We present TriDeNT, a novel self-supervised method for utilising privileged data that is not available during inference to improve performance.
More From Less: Self-Supervised Knowledge Distillation for Routine Histopathology Data
Medical imaging technologies are generating increasingly large amounts of high-quality, information-dense data.
Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides
Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists.
Adversarial learning of cancer tissue representations
We present an adversarial learning model to extract feature representations of cancer tissue, without the need for manual annotations.
MathBERT: A Pre-Trained Model for Mathematical Formula Understanding
Large-scale pre-trained models like BERT, have obtained a great success in various Natural Language Processing (NLP) tasks, while it is still a challenge to adapt them to the math-related tasks.
Automatic Description Construction for Math Expression via Topic Relation Graph
In the Selector, a Topic Relation Graph (TRG) is proposed to obtain the relevant documents which contain the comprehensive information of math expressions.
ConvMath: A Convolutional Sequence Network for Mathematical Expression Recognition
Despite the recent advances in optical character recognition (OCR), mathematical expressions still face a great challenge to recognize due to their two-dimensional graphical layout.
Optical Character Recognition
Optical Character Recognition (OCR)
Learning a low dimensional manifold of real cancer tissue with PathologyGAN
We present a deep generative model that learns to simulate high-fidelity cancer tissue images while mapping the real images onto an interpretable low dimensional latent space.
Automatic Generation of Headlines for Online Math Questions
Compared to conventional summarization tasks, this task has two extra and essential constraints: 1) Detailed math questions consist of text and math equations which require a unified framework to jointly model textual and mathematical information; 2) Unlike text, math equations contain semantic and structural features, and both of them should be captured together.
PathologyGAN: Learning deep representations of cancer tissue
We show that our model generates high quality images, with a FID of 16. 65 (breast cancer) and 32. 05 (colorectal cancer).
