4.7 Article

Data and knowledge co-driving for cancer subtype classification on multi-scale histopathological slides

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Engineering, Biomedical

Real-time application based CNN architecture for automatic USCT bone image segmentation

Marwa Fradi et al.

Summary: Artificial Intelligence has achieved significant success in medical image analysis, especially in the ultrasound field. A new segmentation application based on various Convolutional Neural Network models for Ultrasonic Computed Tomographic images has been developed, achieving high segmentation accuracy. The system includes USCT data augmentation techniques, implementation on GPU, and is suitable for medical real-time applications.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Proceedings Paper Computer Science, Artificial Intelligence

T-Net: A Resource-Constrained Tiny Convolutional Neural Network for Medical Image Segmentation

Tariq M. Khan et al.

Summary: This paper presents T-Net, a fully convolutional network designed for resource constrained and mobile devices. T-Net uses group convolutions and skip connections to enhance its performance, and employs dice loss for pixel-wise classification to alleviate the effect of class imbalance. Experimental results show that T-Net outperforms alternatives with much larger trainable parameters in various applications.

2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022) (2022)

Article Computer Science, Information Systems

Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images

Haijun Lei et al.

Summary: The text discusses a method using deep convolutional neural networks to automatically detect mitosis, identifying mitotic candidates for screening and achieving the best detection results on the dataset of the International Pattern Recognition Conference (ICPR) 2012 Mitosis Detection Competition.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Biochemistry & Molecular Biology

A Hybrid-Attention Nested UNet for Nuclear Segmentation in Histopathological Images

Hongliang He et al.

Summary: This study proposes a hybrid-attention nested UNet (Han-Net) model to address the challenge of nuclear segmentation in histopathological images. By combining a hybrid nested U-shaped network and a hybrid attention block, Han-Net extracts discriminative features and effectively segments various types of nuclei. The proposed model achieves state-of-the-art performance in a publicly available multi-organ dataset.

FRONTIERS IN MOLECULAR BIOSCIENCES (2021)

Article Computer Science, Theory & Methods

Recurrent Neural Networks for Edge Intelligence: A Survey

Varsha S. Lalapura et al.

Summary: Recurrent Neural Networks are widely used in artificial intelligence applications, but training them poses challenges. The expansion of IoT requires intelligent models to be deployed at the edge, with compression techniques playing a key role.

ACM COMPUTING SURVEYS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images

Noriaki Hashimoto et al.

2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2020)

Article Computer Science, Information Systems

Unsupervised Learning for Cell-Level Visual Representation in Histopathology Images With Generative Adversarial Networks

Bo Hu et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2019)

Article Computer Science, Interdisciplinary Applications

From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge

Peter Bandi et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2019)

Article Computer Science, Artificial Intelligence

Multiple instance learning for histopathological breast cancer image classification

P. J. Sudharshan et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Biotechnology & Applied Microbiology

Unsupervised Domain Adaptation for Classification of Histopathology Whole-Slide Images

Jian Ren et al.

FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Multi-Stage Pathological Image Classification using Semantic Segmentation

Shusuke Takahama et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology

Hiroki Tokunaga et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Proceedings Paper Computer Science, Interdisciplinary Applications

Improving Classification of Breast Cancer by Utilizing the Image Pyramids of Whole-Slide Imaging and Multi-Scale Convolutional Neural Networks

Li Tong et al.

2019 IEEE 43RD ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 1 (2019)

Article Engineering, Biomedical

Representation learning-based unsupervised domain adaptation for classification of breast cancer histopathology images

Pendar Alirezazadeh et al.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2018)

Article Medicine, General & Internal

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer

Babak Ehteshami Bejnordi et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2017)

Article Computer Science, Interdisciplinary Applications

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Hoo-Chang Shin et al.

IEEE TRANSACTIONS ON MEDICAL IMAGING (2016)