A survey on deep learning in medical image analysis

  title={A survey on deep learning in medical image analysis},
  author={Geert J. S. Litjens and Thijs Kooi and Babak Ehteshami Bejnordi and Arnaud Arindra Adiyoso Setio and Francesco Ciompi and Mohsen Ghafoorian and Jeroen van der Laak and Bram van Ginneken and Clara I. S{\'a}nchez},
  journal={Medical image analysis},


This study reviews literature studies of recent years that utilized Deep Learning algorithms on medical images in order to present a general picture of the relevant literature.

A Review on Medical Image Analysis with Convolutional Neural Networks

  • Paarth BirV. Balas
  • Computer Science
    2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON)
  • 2020
A few popular algorithms using Convolutional Neural Networks being used in the field along with their applications: Classification, Detection, Segmentation, Registration and Image Enhancement are introduced.

Deep Learning Applications in Medical Image Analysis

This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field, covering key research areas and applications of medical image classification, localization, detection, segmentation, and registration.

Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis

This chapter focuses on applications of deep learning in microscopy image analysis and digital pathology, in particular, and provides an overview of the state-of-the-art methods and exemplify some of the main techniques.

Automatic Analysis of Lesion in Cardiovascular Image using Fully Convolutional Neural Networks

This paper introduces the automatic analysis of lesions in cardiovascular OCT images which few people ever did before and proposes an architecture that includes two task branches, a classification network and a regression network, which can effectively identify the region of interest in the image through joint training end-to-end.

Deep Learning for Cardiac Image Segmentation: A Review

A review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging, computed tomography, and ultrasound and major anatomical structures of interest.

State-of-the-Art Review of Deep Learning for Medical Image Analysis

A concise discussion of different techniques that are being deployed using deep learning for medical imaging such as classification, detection, & segmentation is investigated.

Promises and limitations of deep learning for medical image segmentation

Deep learning methods are pervasive throughout the entire medical imaging community, with Convolutional Neural Networks (CNNs) being the most used model for tasks such as dense prediction, detection and classification.

Deep Learning Techniques for Biomedical Image Analysis in Healthcare

This chapter summarizes a review of different deep learning techniques used and how they are applied in medical image interpretation and future directions.



Deep Learning in Medical Image Analysis.

This review covers computer-assisted analysis of images in the field of medical imaging and introduces the fundamentals of deep learning methods and their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.

Deep Neural Networks for Fast Segmentation of 3D Medical Images

A CNN for 3D volume segmentation based on recently introduced deep learning components will be presented and the usage of orthogonal patches, which combine shape and locality information with intensity information for CNN training will be evaluated.

Understanding the Mechanisms of Deep Transfer Learning for Medical Images

It is shown that a transferred and tuned CNN can outperform a state-of-the-art feature engineered pipeline and a hybridization of these two techniques achieves 20 % higher performance.

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

The papers in this special section focus on the technology and applications supported by deep learning, which have proven to be powerful tools for a broad range of computer vision tasks.

A Perspective on Deep Imaging

To realize the full impact of machine learning for tomographic imaging, major theoretical, technical and translational efforts are immediately needed.

Anatomy-specific classification of medical images using deep convolutional nets

It is demonstrated that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis and a data augmentation approach can help to enrich the data set and improve classification performance.

Medical Image Description Using Multi-task-loss CNN

A new multi-task convolutional neural network approach for detection and semantic description of lesions in diagnostic images is presented with a proposed CNN-based architecture trained to generate and rank rectangular regions of interests surrounding suspicious areas.

Computational mammography using deep neural networks

This work deals with mammography images and presents a novel supervised deep learning-based framework for region classification into semantically coherent tissues using Convolutional Neural Network to learn discriminative features automatically.

Deep vessel tracking: A generalized probabilistic approach via deep learning

Qualitative and quantitative results over retinal fundus data demonstrate that the proposed framework achieves comparable accuracy as compared with state-of-the-art methods, while efficiently producing more information regarding the candidate tree structure.

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

This paper considered four distinct medical imaging applications in three specialties involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner.