Deep Learning in Healthcare: An In-Depth Analysis
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Abstract (arXiv)
Deep learning (DL) along with never-ending advancements in computational processing and cloud technologies have bestowed us powerful analyzing tools and techniques in the past decade and enabled us to use and apply them in various fields of study. Health informatics is not an exception, and conversely, is the discipline that generates the most amount of data in today's era and can benefit from DL the most. Extracting features and finding complex patterns from a huge amount of raw data and transforming them into knowledge is a challenging task. Besides, various DL architectures have been proposed by researchers throughout the years to tackle different problems. In this paper, we provide a review of DL models and their broad application in bioinformatics and healthcare categorized by their architecture. In addition, we also go over some of the key challenges that still exist and can show up while conducting DL research.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 37 of its 117 references that are in the map. First posted on arXiv 12 Feb 2023.
Builds on
37 of 117 references.
References · 37
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2017Mask R-CNNcited by 7,277
- 2022Generative Adversarial Networkscited by 5,627
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,859
- 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
- 2015Fast R-CNNcited by 2,933
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
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- 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
- 2014How transferable are features in deep neural networks?cited by 1,225
- 2015Multi-Scale Context Aggregation by Dilated Convolutionscited by 1,133
- 2017Residual Attention Network for Image Classificationcited by 386
- 2018Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentationcited by 145
- 2016The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentationcited by 122
- 2019Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Eracited by 87
- 2016Medical image denoising using convolutional denoising autoencoderscited by 76
- 20192D3D-MatchNet: Learning to Match Keypoints Across 2D Image and 3D Point Cloudcited by 62
- 2018DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classificationcited by 36
- 2015Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networkscited by 17
- 2019Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopycited by 9
- 2017Automated sub-cortical brain structure segmentation combining spatial and deep convolutional featurescited by 8
- 2020DeepMSRF: A novel Deep Multimodal Speaker Recognition framework with Feature selectioncited by 8
- 2020Deep Reinforcement Learning for Organ Localization in CTcited by 7
- 2017Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemescited by 6
- 2019A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI imagescited by 6
- 2020A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification studycited by 6
- 2020DRDr II: Detecting the Severity Level of Diabetic Retinopathy Using Mask RCNN and Transfer Learningcited by 6
- 2021Embodied AI-Driven Operation of Smart Cities: A Concise Reviewcited by 6
- 2020DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learningcited by 5
- 2021DRDrV3: Complete Lesion Detection in Fundus Images Using Mask R-CNN, Transfer Learning, and LSTMcited by 4
- 2021Data Analytics for Smart cities: Challenges and Promisescited by 3
- 2015Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencodercited by 2
- 2019Teaching deep neural networks to localize single molecules for super-resolution microscopycited by 1
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