Search Results for author:
Found 10 papers, 1 papers with code
Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data
Deep semi-supervised learning (SSL) has been shown very effectively.
Breast Cancer Image Classification Method Based on Deep Transfer Learning
To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and transfer learning is proposed.
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example
To address the challenges of dependence on pathologists expertise and the time-consuming nature of achieving accurate breast pathological image classification, this paper introduces an approach utilizing convolutional neural networks (CNNs) for the rapid categorization of pathological images, aiming to enhance the efficiency of breast pathological image detection.
Survival Prediction Across Diverse Cancer Types Using Neural Networks
This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
REFRAME: Reflective Surface Real-Time Rendering for Mobile Devices
This work tackles the challenging task of achieving real-time novel view synthesis on various scenes, including highly reflective objects and unbounded outdoor scenes.
SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic
Scalable service-Oriented Middleware over IP (SOME/IP) is an Ethernet communication standard protocol in the Automotive Open System Architecture (AUTOSAR), promoting ECU-to-ECU communication over the IP stack.
Learning Image Deraining Transformer Network with Dynamic Dual Self-Attention
Recently, Transformer-based architecture has been introduced into single image deraining task due to its advantage in modeling non-local information.
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning
Then these negative adversaries are trained end-to-end together with the backbone representation network to enhance the discriminative information and promote factor disentanglement performance by maximizing the adversarial contrastive loss.
Unpaired Deep Image Deraining Using Dual Contrastive Learning
Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible.
Estimating the black hole spin for the X-ray binary MAXI J1820+070
MAXI J1820+070 is a newly-discovered black hole X-ray binary, whose dynamical parameters, namely the black hole mass, the inclination angle and the source distance, have been estimated recently.
High Energy Astrophysical Phenomena
