WPI- Computer Science Department, PhD Defense Ziyang Liu " Neural Networks Models for Multi-Attribute Assessment of Fine-Grained Wound Images"


Computer Science

Ziyang Liu

PhD Candidate

WPI – Computer Science


Wednesday, April 12, 2023

Time: 10:30 a.m. – 11:30 a.m.

Location: Fuller Labs 141


Committee Members:

Advisor: Prof. Emmanuel Agu, WPI – Computer Science

Prof. Michael Gennert

Prof. Dmitry Korkin

Prof. Jacob Whitehill

Prof. Bengisu Tulu


Abstract :

Chronicwounds affect 6.5 million Americans and 15 percent of Medicare patients. Many wound patients are treated by nurses who visit their homes periodically. Care of chronic wounds involves cleaning, debridement, changing of dressings and applying medicines. Moreover, human error can also occur. Due to the large and growing number of chronic wounds, there is an increasing demand for more efficient chronic wound care, especially information technology solutions that support the work of medical personnel and reduce the cost of care.

This dissertation focuses on researching the SmartWAnDS module that autonomously grades the healing progress of four wound types (diabetic ulcers, pressure ulcers, vascular ulcers and surgical wounds) from their visual appearance in a smartphone photograph. Novel neural networks-based solutions are proposed for three specific problems:

1) Multi-attribute, comprehensive wound assessments from smartphone images: A DenseNet Convolutional Neural Network (CNN) framework with patch-based context-preserving attention is proposed to assess all eight PWAT attributes.

2) Wound infection and ischemia detection: a Diabetic Foot Ulcer (DFU) dataset was augmented using geometric and color image operations, after which binary infection and ischemia classification was done using the EfficientNet deep learning model.

3) Robust multi-attribute wound assessment using small, imbalanced, wound dataset: A Semi-Supervised learning and Progressive Multi-Granularity training mechanism were augment a small primary labeled dataset using a secondary corpus of unlabeled wound images. Multi-attribute wound scoring utilized the EfficientNet CNN on the augmented wound corpus.

The proposed SmartWAnDS system is the first intelligent system that autonomously grades wounds based on the eight criteria in the PWAT rubric, alleviating the significant burden that manual wound grading imposes on wound care nurses.