Medical Hyperspectral Image Classification Based on End-to-End Fusion Deep Neural Network

Trends in Deep Learning for Medical Hyperspectral Image Analysis

“…Cell classification using convolutional neural networks in medical hyperspectral imagery [45] Classification Hyperspectral imaging for cancer detection and classification [47] Medical hyperspectral imaging: a review [7] Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging [35] Deep learning based classification for head and neck cancer detection with hyperspectral imaging in an animal model [55] Convolutional neural network for medical hyperspectral image classification with kernel fusion [51] Classification Blood cell classification based on hyperspectral imaging with modulated Gabor and CNN [52] Medical hyperspectral image classification based on end-to-end fusion deep neural network [53] Tissue classification of oncologic esophageal resectates based on hyperspectral data [49] Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm [57] Design of a multilayer neural network for the classification of skin ulcers' hyperspectral images: a proof of concept [48] Hyperspectral imaging based method for fast characterization of kidney stone types [46] Design of a multilayer neural network for the classification of skin ulcers' hyperspectral images: a proof of concept [83] Non-invasive skin cancer diagnosis using hyperspectral imaging for in-situ clinical support [50] Hyperspectral imaging for colon cancer classification in surgical specimens: towards optical biopsy during image-guided surgery [84] Deep learning applied to hyperspectral endoscopy for online spectral classification [85] Spectral-spatial recurrent-convolutional networks for in-vivo hyperspectral tumor type classification [56] Blood stain classification with hyperspectral imaging and deep neural networks [54] Hyperspectral imaging for glioblastoma surgery: improving tumor identification using a deep spectral-spatial approach [58] Dual-modality endoscopic probe for tissue surface shape reconstruction and hyperspectral imaging enabled by deep neural networks [62] Detection Probe-based rapid hybrid hyperspectral and tissue surface imaging aided by fully convolutional networks [63] A dual stream network for tumor detection in hyperspectral images [59] Adaptive deep learning for head and neck cancer detection using hyperspectral imaging [67] Detection Hyperspectral imaging of head and neck squamous cell carcinoma for can...…”

“…Hyperspectral system for imaging of skin chromophores and blood oxygenation [79] Other Estimation of tissue oxygen saturation from RGB images and sparse hyperspectral signals based on conditional generative adversarial network [78] Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology [81] Generating hyperspectral skin cancer imagery using generative adversarial neural network [82] CNN-based model used for endoscopic image reconstruction to enhance surgical guidance [62] Classifying cancerous tissue samples from neck and head regions using CNN [35] Detection of neck and head cancerous cells via classification using CNN [55] Improvisation of CNN using kernel fusion implemented for cell classification [51] Implementation of CNN for blood cell classification [52] Two-channel CNN for solving limited-samples problem for CNN models [53] Use of CNN to detect squamous cell carcinoma between samples from different patients [65] CNN used for detection of oral cancer [57] Using specular glare in MHSI along with CNN to detect squamous cell carcinoma [66] Another study for CNN to detect squamous cell carcinoma [61] Detection of brain tumor with the aid of CNN [71] CNN Detecting carcinoma thyroid sample with the aid of CNN [60] Different CNN models compared to one another for classifying skin cancer from patient data HIS [72] CNN utilized to classify and detect squamous cell carcinoma [68] CNN used to classify and detect breast cancer cells [70] CNN implemented for detection of Glioblastoma cells from Hematoxylin & Eosin tissue sample [86] In-vivo Laryngeal cancer detection based on CNN [69] Convolutional based RetinaNet model implemented to classify and detect tumors in epithelial tissue [87] Implemented a hybrid CNN model to classify colon tumor in order to aid surgical guidance [84] Five-layer CNN applied to classify endoscopy HSI [85] Study of classification for blood and similar appearing substances from HSI with CNN,RNN, MLP [54] Proposed framework of 3D-2D CNN-based approach to classify brain tumors [58] ANN Implementation of ANN and SVM for cancerous cell HSI <...>…”

“…A generalized process for the classification 51process for cancerous cells is displayed in Fig.9. MHSI in a similar fashion[45] [52][53], where increased pixel size for the MHSI produced better results with respect to classification accuracy, thus further proving the potential for CNN in MHSI. While all three papers implement pixel-patch input approaches with varying numbers of samples, the proposed two-channel end-to-end framework…”

Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2 Imagery and a Hierarchical Cascade Convolutional Neural Network

“…Thus, it would be helpful for the classification of objects or geographical landscapes with complex components from HSR imagery, such as buildings [59,60], urban function zones [61], and fashions of rural settlements [62]. In addition, some analyses of natural imagery may also benefit from such structures, such as the identification of urban street scenes [63,64], agricultural trees [65], cells [66,67], and bacteria [68,69]. In addition, as the attention-based module is very helpful for the neural networks to find the most representative parts from abundant features, it can also be helpful for the feature space refinement in high spectral resolution image applications [70].…”

Adversarially Robust Hyperspectral Image Classification via Random Spectral Sampling and Spectral Shape Encoding

“…Therefore, HSIs contain a discriminative spectral characteristic across the wavelength for each material [1]- [3]. Such an advantage of rich spectral information can help the HSI classification to identify every pixel of HSI (i.e., ground objects in HSI), and it has been applied into various applications, such as environment management, medical diagnosis, and ground surveillance [4]- [8].…”