Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective

@article{Wen2020DoNB,
  title={Do Noises Bother Human and Neural Networks In the Same Way? A Medical Image Analysis Perspective},
  author={Shao-Cheng Wen and Yu-Jen Chen and Zihao Liu and Wujie Wen and Xiaowei Xu and Yiyu Shi and Tsung-Yi Ho and Qianjun Jia and Meiping Huang and Jian Zhuang},
  journal={2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)},
  year={2020},
  pages={1166-1170}
}
Deep learning had already demonstrated its power in medical images, including denoising, classification, segmentation, etc. All these applications are proposed to automatically analyze medical images beforehand, which brings more information to radiologists during clinical assessment for accuracy improvement. Recently, many medical denoising methods had shown their significant artifact reduction result and noise removal both quantitatively and qualitatively. However, those existing methods are… 

Ct Image Denoising With Encoder-Decoder Based Graph Convolutional Networks

TLDR
This paper proposes an encoder-decoder-based graph convolutional network (ED-GCN) for CT image denoising that combines local convolutions and graph convolutions to process both local and non-local features.

References

SHOWING 1-10 OF 27 REFERENCES

Connecting Image Denoising and High-Level Vision Tasks via Deep Learning

TLDR
A convolutional neural network in which convolutions are conducted in various spatial resolutions via downsampling and upsampling operations in order to fuse and exploit contextual information on different scales and the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning is demonstrated.

Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation

  • Xiaowei XuQ. Lu Yiyu Shi
  • Computer Science
    2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
  • 2018
TLDR
This paper applies quantization techniques to FCNs for accurate biomedical image segmentation with a focus on a state-of-the-art segmentation framework, suggestive annotation, which judiciously extracts representative annotation samples from the original training dataset, obtaining an effective small-sized balanced training dataset.

Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network

TLDR
The proposed MCDnCNN model has been demonstrated to robustly denoise three dimensional MR images with Rician noise to show the most robust denoising performance in all three datasets.

Zero-Shot Medical Image Artifact Reduction

TLDR
This paper introduces a “Zero-Shot” medical image Artifact Reduction (ZSAR) framework, which leverages the power of deep learning but without using general pre-trained networks or any clean image reference, and is the first deep learning framework that reduces artifacts in medical images without using a priori training set.

Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

TLDR
This work combines the autoencoder, deconvolution network, and shortcut connections into the residual encoder–decoder convolutional neural network (RED-CNN) for low-dose CT imaging and achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases.

Uncertainty-Aware Training of Neural Networks for Selective Medical Image Segmentation

TLDR
A novel method is presented that considers such uncertainty in the training process to maximize the accuracy on the confident subset rather than the Accuracy on the whole dataset.

Data Augmentation Using Learned Transformations for One-Shot Medical Image Segmentation

TLDR
This work learns a model of transformations from the images, and uses the model along with the labeled example to synthesize additional labeled examples, enabling the synthesis of complex effects such as variations in anatomy and image acquisition procedures.

A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI

TLDR
A novel approach to guide the low-level visual task using the information from mid- or high-level task with the help of a segmentation-aware deep fusion network called SADFN for compressed sensing MRI.

ICA-UNet: ICA Inspired Statistical UNet for Real-time 3D Cardiac Cine MRI Segmentation

TLDR
Experiments show that, compared with the state-of-the-arts, ICA-UNet not only achieves higher Dice scores, but also meets the real-time requirements for both throughput and latency (up to 12.6\(\times \) reduction), enabling real- time guidance for cardiac interventions without visual lag.

Multi-Classification of Brain Tumor Images Using Deep Neural Network

TLDR
A DL model based on a convolutional neural network is proposed to classify different brain tumor types using two publicly available datasets and the results indicate the ability of the model for brain tumor multi-classification purposes.