Search Results for author:
Found 14 papers, 2 papers with code
Distributionally Robust Evaluation for Real-Time Flexibility of Electric Vehicles Considering Uncertain Departure Behavior and State-of-Charge
Accurately evaluating the real-time flexibility of electric vehicles (EVs) is necessary for EV aggregators to offer ancillary services.
IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach
In addition, we conducted an ablation experiment and an interchangeability experiment to verify the ability and interchangeability of the three channels.
Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer
The gold standard for gastric cancer detection is gastric histopathological image analysis, but there are certain drawbacks in the existing histopathological detection and diagnosis.
Histopathological Image Classification
Image Classification
+3
Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology
Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images.
Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review
Electronic health records (EHRs), digital collections of patient healthcare events and observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and research.
GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection
In this paper, a multi-scale visual transformer model, referred as GasHis-Transformer, is proposed for Gastric Histopathological Image Detection (GHID), which enables the automatic global detection of gastric cancer images.
A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification
The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module.
A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis
In this review, we present a comprehensive overview of pathology image analysis based on the markov random fields (MRFs) and conditional random fields (CRFs), which are two popular random field models.
Global Data Science Project for COVID-19
At international level, due to the travel restrictions, the number of international flights has plunged overall at around 88 percent during March.
What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
The outbreak of coronavirus disease 2019 (COVID-19) recently has affected human life to a great extent.
Gastric histopathology image segmentation using a hierarchical conditional random field
This HCRF model is built up with higher order potentials, including pixel-level and patch-level potentials, and graph-based post-processing is applied to further improve its segmentation performance.
Hard-Aware Fashion Attribute Classification
For "hard" attributes with insufficient training data, Deact brings more stable synthetic samples for training and further improve the performance.
Spatial-Aware Non-Local Attention for Fashion Landmark Detection
Fashion landmark detection is a challenging task even using the current deep learning techniques, due to the large variation and non-rigid deformation of clothes.
AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding
Significant progress has been achieved in Computer Vision by leveraging large-scale image datasets.
