Deep Learning in Microscopy Image Analysis: A Survey

By Fuyong Xing, Yuanpu Xie, H. Su, Fujun Liu, Lin F. Yang. IEEE Transactions on Neural Networks and Learning Systems, 2018

392 citations recorded by the source corpus.

Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerging as a leading machine learning tool in computer vision and has attracted considerable attention in biomedical image analysis. In this paper, we provide a snapshot of this fast-growing field, specifically for microscopy image analysis. We briefly introduce the popular deep neural networks and summarize current deep learning achievements in various tasks, such as detection, segmentation, and classification in microscopy image analysis. In particular, we explain the architectures and the principles of convolutional neural networks, fully convolutional networks, recurrent neural networks, stacked autoencoders, and deep belief networks, and interpret their formulations or modelings for specific tasks on various microscopy images. In addition, we discuss the open challenges and the potential trends of future research in microscopy image analysis using deep learning.

Subfields: Artificial Intelligence, Biophysics, Media Technology

View Paper: https://www.semanticscholar.org/paper/08dc94471605308669c8d3d8284ba94fcc93e345

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. Shuaicheng Niu
4. Yifan Zhang
5. Junzhou Huang
6. Khan Muhammad
7. Salman Khan
8. Dong Ni

In Biophysics

1. Ming Liang
2. Jianhua Yao
3. Junzhou Huang
4. Xiaolin Hu
5. Fangzhou Liao
6. Xin Yang

In Media Technology

1. Khan Muhammad
2. Peilin Zhao
3. Salman Khan
4. Ming Liang
5. Ying Wei
6. Jianhua Yao
7. Junzhou Huang
8. Yifan Zhang

Citations in the corpus

• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (2019, background): “is often absent due to prohibitive costs of data labeling [12], [13], which impedes successful applications of deep learning.”
• Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network (2017, background): “Recent advancement of deep learning has enabled computer vision models to help the doctors to diagnose various problems; in some cases, the models have exhibited competitive performance to doctors [3]–[7].”
• A Review of Machine Learning and Deep Learning Applications (2018, methodology): “Microscopy Image Analysis is often done by exploitation Deep Neural Network as can be seen from this paper [20].”
• From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification (2019, background): “Histopathology image is a gold standard for clinical diagnosis of cancer [1,2].”
• Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, background): “Traditionally, a specialist analyzes medical images and, manually, estimates the probability of developing a tumor [11].”
• Deep Learning in Medical Ultrasound Analysis: A Review (2019, background): “Several review articles have been written to date on the application of deep learning to medical image analysis; these articles focus on either the whole field of medical image analysis [11–15] or other single-imaging modalities such as MRI [53] and microscopy [54].”