Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

By M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Fathima Syeda-Mahmood, João Manuel Ribeiro da Silva Tavares, M. Moradi, Andrew P. Bradley, Hayit Greenspan, João Paulo Papa, Anant Madabhushi and 5 more. Lecture Notes in Computer Science, 2017

432 citations recorded by the source corpus.

Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance that hampers the generalization of automatic image analysis methods. Typically, this is addressed with ad-hoc approaches such as staining normalization that aim to reduce the appearance variability. In this paper, we propose a systematic solution based on domain-adversarial neural networks. We hypothesize that removing the domain information from the model representation leads to better generalization. We tested our hypothesis for the problem of mitosis detection in breast cancer histopathology images and made a comparative analysis with two other approaches. We show that combining color augmentation with domain-adversarial training is a better alternative than standard approaches to improve the generalization of deep learning methods.

Subfields: Artificial Intelligence, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4f5bed48a00719fbbba05b6b58116a0b0dae87ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mingkui Tan
2. Peilin Zhao
3. Yifan Zhang
4. Junzhou Huang
5. Bin Zheng
6. Robert S. Mannel
7. Kecheng Zhang
8. Ying Wei

In Biomedical Engineering

1. Peilin Zhao
2. Junzhou Huang
3. Jiezhang Cao

In Radiology, Nuclear Medicine and Imaging

1. Peilin Zhao
2. Junzhou Huang
3. Yifan Zhang
4. Mingkui Tan

Citations in the corpus

• From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification (2019, methodology): “However, most DUDA methods for general images [11–13] and for WSIs [14,16,17] neglect the intra-domain discrepancy and class imbalance, hence performing poorly in this task.”
• Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “Common computer-aided detection (CADe) tasks include detecting lung nodules (Gu et al., 2018; Xie et al., 2019b), breast masses (Akselrod-Ballin et al., 2017; Ribli et al., 2018), lymph nodes (Lin et al., 2019; Zhu et al., 2020b), sclerosis lesions (Nair et al., 2020), etc.”