Search Results for author:
Found 17 papers, 3 papers with code
CathAction: A Benchmark for Endovascular Intervention Understanding
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions.
ProMISe: Promptable Medical Image Segmentation using SAM
Our experiments demonstrate that such adaptive prompts significantly improve SAM's non-fine-tuned performance in MIS.
Ranked #1 on
Medical Image Segmentation
on ISIC 2018
Combining Transformer based Deep Reinforcement Learning with Black-Litterman Model for Portfolio Optimization
However, typical DRL agents for portfolio optimization cannot learn a policy that is aware of the dynamic correlation between portfolio asset returns.
CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy
To the best of our knowledge, our approach is the first to combine clustering methods and reinforcement learning methods for portfolio management in the context of multi-period trading.
Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning
In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training).
Distortion-Disentangled Contrastive Learning
The POCL method typically uses a single loss function to extract the distortion invariant representation (DIR) which describes the proximity of positive-pair representations affected by different distortions.
DualStreamFoveaNet: A Dual Stream Fusion Architecture with Anatomical Awareness for Robust Fovea Localization
Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss.
DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation
The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively.
Ranked #2 on
Medical Image Segmentation
on 2018 Data Science Bowl
CNN-based Classification Framework for Lung Tissues with Auxiliary Information
Interstitial lung diseases are a large group of heterogeneous diseases characterized by different degrees of alveolitis and pulmonary fibrosis.
Stepwise Feature Fusion: Local Guides Global
However, due to the structure of polyps image and the varying shapes of polyps, it easy for existing deep learning models to overfitting the current dataset.
Ranked #3 on
Medical Image Segmentation
on 2018 Data Science Bowl
A Robust Framework of Chromosome Straightening with ViT-Patch GAN
The generator learns the motion representation of chromosomes for straightening.
Bilateral-ViT for Robust Fovea Localization
The fovea is an important anatomical landmark of the retina.
A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management
We compare the compound annual return rate of our strategy against seven other strategies, e. g., Uniform Buy and Hold, Exponential Gradient and Universal Portfolios.
A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image
To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details.
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data
The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies.
Application of Deep Q-Network in Portfolio Management
This paper will introduce a strategy based on the classic Deep Reinforcement Learning algorithm, Deep Q-Network, for portfolio management in stock market.
News2vec: News Network Embedding with Subnode Information
With the development of NLP technologies, news can be automatically categorized and labeled according to a variety of characteristics, at the same time be represented as low dimensional embeddings.
