Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images

@article{Bi2022DeepMR,
  title={Deep Multi-Scale Resemblance Network for the Sub-class Differentiation of Adrenal Masses on Computed Tomography Images},
  author={Lei Bi and Jinman Kim and Tingwei Su and Michael J. Fulham and David Dagan Feng and Guang Ning},
  journal={ArXiv},
  year={2022},
  volume={abs/2007.14625}
}

Figures and Tables from this paper

References

SHOWING 1-10 OF 47 REFERENCES

Attention Residual Learning for Skin Lesion Classification

TLDR
The results indicate that the proposed ARL-CNN model can adaptively focus on the discriminative parts of skin lesions, and thus achieve the state-of-the-art performance in skin lesion classification.

Medical image classification using synergic deep learning

An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification

TLDR
A new method for classifying medical images that uses an ensemble of different convolutional neural network (CNN) architectures that achieves a higher accuracy than established CNNs and is only overtaken by those methods that source additional training data.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

TLDR
A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset, and demonstrated the potential of CNNs in analyzing lung patterns.

Automatic Scoring of Multiple Semantic Attributes With Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images

TLDR
The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists’ ratings than the scores from single-task LASSO and elastic net regression methods, which may provide richer quantitative assessments of nodules for better support of diagnostic decision and management.

Deep Transfer Learning for Modality Classification of Medical Images

TLDR
New, state-of-the-art results are obtained which imply that CNNs, based on the proposed transfer learning methods and data augmentation skills, can identify more efficiently modalities of medical images.

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

TLDR
This paper proposes a novel automatic method to detect CMBs from magnetic resonance (MR) images by exploiting the 3D convolutional neural network (CNN), outperforming previous methods using low-level descriptors or 2D CNNs by a significant margin.

Classification of Medical Images in the Biomedical Literature by Jointly Using Deep and Handcrafted Visual Features

TLDR
A combined deep and handcrafted visual feature (CDHVF) based algorithm that uses features learned by three fine-tuned and pretrained deep convolutional neural networks and two handcrafted descriptors in a joint approach to improve accuracy in certain medical image classification problems is proposed.