Research Connect

TL;DR

The possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement, and the potential use ofDeep learning for spectroscopic data including spectral data preprocessing and spectral classification are discussed.

Abstract

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state‐of‐the‐art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real‐time biophotonic decision‐making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

Authors

Pranita Pradhan

1 Paper

Shuxia Guo

1 Paper

Oleg Ryabchykov

1 Paper

TL;DR

Abstract

Authors

References131 items

ImageNet: A large-scale hierarchical image database

Double-slit photoelectron interference in strong-field ionization of the neon dimer

How transferable are features in deep neural networks?

Deep Learning: Methods and Applications

Deep Learning

A survey on deep learning in medical image analysis

U-Net: Convolutional Networks for Biomedical Image Segmentation

Representation Learning: A Review and New Perspectives

GENERATIVE ADVERSARIAL NETS

Shape and arrangement of columns in cat's striate cortex

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Large-Scale Machine Learning with Stochastic Gradient Descent

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Learning representations by back-propagating errors

Methods for interpreting and understanding deep neural networks

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

The perceptron: a probabilistic model for information storage and organization in the brain.

Dropout: a simple way to prevent neural networks from overfitting

Author ' s personal copy A Fast Quartet tree heuristic for hierarchical clustering

A survey on Image Data Augmentation for Deep Learning

Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases

Rethinking the Inception Architecture for Computer Vision

Survey on deep learning with class imbalance

Opportunities and obstacles for deep learning in biology and medicine

Deep learning enables cross-modality super-resolution in fluorescence microscopy

Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

Single‐layer multiple‐kernel‐based convolutional neural network for biological Raman spectral analysis

Automatic label‐free detection of breast cancer using nonlinear multimodal imaging and the convolutional neural network ResNet50

A practical convolutional neural network model for discriminating Raman spectra of human and animal blood

Fast Non-invasive Diagnosis of Cervical Cancer by Coherent anti-Stokes Raman Scattering.

2019 International Joint Conference on Neural Networks (IJCNN)

Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy.

A low‐cost, automated parasite diagnostic system via a portable, robotic microscope and deep learning

Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks.

Speeding Up the Line-Scan Raman Imaging of Living Cells by Deep Convolutional Neural Network.

Simultaneous denoising and super-resolution of optical coherence tomography images based on generative adversarial network.

LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks

Automated classification of hepatocellular carcinoma differentiation using multiphoton microscopy and deep learning

Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.

Deep representation learning for domain adaptatable classification of infrared spectral imaging data

A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy Images

Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network

Retinal optical coherence tomography image enhancement via deep learning.

Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging

An introduction to neural networks

Speckle noise reduction in optical coherence tomography images based on edge-sensitive cGAN.

Coal analysis based on visible-infrared spectroscopy and a deep neural network

Tackling standardization in fluorescence molecular imaging

Identification and characterization of bladder cancer by low‐resolution fiber‐optic Raman spectroscopy

Data augmentation for improving deep learning in image classification problem

Sparse Representation Classification of Tobacco Leaves Using Near-Infrared Spectroscopy and a Deep Learning Algorithm

Model transfer for Raman‐spectroscopy‐based bacterial classification

Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning

Deep Learning in Radiology.

Challenges and opportunities in clinical translation of biomedical optical spectroscopy and imaging

Convolutional neural network‐based malaria diagnosis from focus stack of blood smear images acquired using custom‐built slide scanner

Hierarchical classification of microorganisms based on high‐dimensional phenotypic data

Keeping up standards

Deep-STORM: super-resolution single-molecule microscopy by deep learning

Biophotonics: the big picture

A systematic study of the class imbalance problem in convolutional neural networks

Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer

Towards Virtual H&E Staining of Hyperspectral Lung Histology Images Using Conditional Generative Adversarial Networks

Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain–computer interface: three-class classification of rest, right-, and left-hand motor execution

Training CNNs for Image Registration from Few Samples with Model-based Data Augmentation

Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution

Common mistakes in cross-validating classification models

Deep Learning Microscopy: Enhancing Resolution, Field-of-View and Depth-of-Field of Optical Microscopy Images Using Neural Networks

Convolutional neural networks for vibrational spectroscopic data analysis.

Modern trends in biophotonics for clinical diagnosis and therapy to solve unmet clinical needs

Spatial Clockwork Recurrent Neural Network for Muscle Perimysium Segmentation

On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks

Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

Training deep neural networks on imbalanced data sets

Model-based pre-processing in Raman spectroscopy of biological samples

Standardizing the resolution claims for coherent microscopy