Journal ArticleDOI
Pathologic liver tumor detection using feature aligned multi-scale convolutional network
Tsung-Lung Yang,Hung Wen Tsai,Wei-Che Huang,Jung-Chia Lin,Jia-Bin Liao,Nan Haw Chow,Pau-Choo Chung +6 more
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Wang et al. as mentioned in this paper proposed a Feature Aligned Multi-Scale Convolutional Network (FA-MSCN) architecture for liver tumor detection based on whole slide images (WSI).About:
This article is published in Artificial Intelligence in Medicine.The article was published on 2022-01-01. It has received 10 citations till now. The article focuses on the topics: Medicine & Computer science.read more
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Review on security of federated learning and its application in healthcare
TL;DR: In this paper , the authors analyze the security of federated learning and medical applications and analyze the various risks and attacks faced by the applications, and present a summary and outlook on the application and security of Federated Learning in healthcare.
Proceedings ArticleDOI
Emerging Research Directions of Deep Learning for Pathology Image Analysis
TL;DR: In this paper , a survey of relevant pathology applications is presented, and the research directions of these techniques for future development in pathology image analysis are also presented in this paper. But applying AI methods on pathology image classification is far from easy.
Proceedings ArticleDOI
Emerging Research Directions of Deep Learning for Pathology Image Analysis
TL;DR: In this article , a survey of relevant pathology applications is presented, and the research directions of these techniques for future development in pathology image analysis are also presented in this paper. But applying AI methods on pathology image classification is far from easy and many practical challenging issues arise including pathology analysis under insufficient and inaccurate annotations, recognizing pathology images of different data distributions and training AI models based on decentralized data sources.
Journal ArticleDOI
Application of digital pathology and machine learning in the liver, kidney and lung diseases
Benjamin Wu,Gilbert W. Moeckel +1 more
TL;DR: In this paper , the authors discuss how AI and image analysis not only can grade diseases objectively but also discover aspects of diseases that have prognostic value, and share their vision on the future of digital pathology.
Journal ArticleDOI
A Multi-scale, Multi-region and Attention Mechanism-based Deep Learning Framework for Prediction of Grading in Hepatocellular Carcinoma.
Jingwei Wei,Qian Ji,Yu Gao,Xiaozhen Yang,Donghui Guo,Dongsheng Yu,Chunwang Yuan,Jie Tian,Dawei Ding +8 more
TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) to predict histopathological grading in hepatocellular carcinoma (HCC).
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.