Adaptive deep learning for head and neck cancer detection using hyperspectral imaging.

It can be challenging to detect tumor margins during surgery for complete resection. The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model. Speci

Project Title

Adaptive deep learning for head and neck cancer detection using hyperspectral imaging.

Project Area of Specialization

Artificial Intelligence

Project Summary

It can be challenging to detect tumor margins during surgery for complete resection. The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model. Specifically, an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel. According to the output hypothesis of each pixel, the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights. The auto-encoder network is again trained based on these updated pixels. The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels, and thus can improve the detection performance. The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images. This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection, especially, for the tumor whose margin is indistinct and irregular.

Project Objectives

To develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model.

To extract the deep information.

To create a pixel-wise prediction of cancerous and benign pixel.

Project Implementation Method

The following methods can be used for the fulfill the needs  of  project.

HSI system:

Hyperspectral images were obtained by a wavelength

scanning CRI Maestro in vivo imaging system. This instrument mainly consists of a flexible fiber-optic lighting

system, a solid-state liquid crystal filter, a spectrally optimized lens, and a 16-bit high-resolution charge-coupled

device. For image acquisition, the wavelength setting can

be defined within the range of 450 to 950 nm with 2-nm

increments.

The proposed adaptive deep learning method:

The proposed adaptive deep learning method for cancer

detection on HSI contains four parts:

pre-processing,

deep feature learning,

adaptive weight learning, and

post-processing.

Benefits of the Project

The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images.

Technical Details of Final Deliverable

The automatic detection algorithm will be written and run

in MATLAB on Intel Core 2.60GHz CPU with 16GB of

RAM. The time for normalization, deep feature extraction,

cancer detection, post-processing is about 0.1 s, 2.8 s, 3.2 s,

and 0.02 s, respectively. The total running time is about 6

s for per hyperspectral image. It greatly improved the efficiency of cancer detection compared with the method using 45 min. This automatic cancer detection

method will be & can be implemented in real time if involving the multi-thread, GPU acceleration or parallel programming.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Medical

Other Industries

IT

Core Technology

Artificial Intelligence(AI)

Other Technologies

Internet of Things (IoT)

Sustainable Development Goals

Good Health and Well-Being for People

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com