Knowledge Distillation of Vision Transformers and Convolutional Networks to Predict Inflammatory Bowel Disease

Maurício, José; Domingues, Inês

doi:10.1007/978-3-031-49018-7_27

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-49018-7_27

|View full text |Cite

Knowledge Distillation of Vision Transformers and Convolutional Networks to Predict Inflammatory Bowel Disease

José Maurício,

Inês Domingues

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretability

Maurício,

Domingues

2024

Pattern Anal Applic

View full text Add to dashboard Cite

Crohn’s disease and ulcerative colitis are two chronic diseases that cause inflammation in the tissues of the entire gastrointestinal tract and are described by the term inflammatory bowel disease. Gastroenterologists find it difficult to evaluate endoscopic images to recognise the characteristics of the two chronic diseases. Therefore, this work aims to build a dataset with images of Crohn’s disease and ulcerative colitis (collected from the public datasets LIMUC, HyperKvasir and CrohnIPI) and train deep learning models (five CNNs and six ViTs) to develop a tool capable of helping doctors to distinguish the type of inflammatory bowel disease. In addition, as these architectures will be too heavy to work in a hospital context, in this work, we are looking to use knowledge distillation to create lighter and simpler architectures with the same precision as the pre-trained architectures used in this study. During this process, it is important to evaluate and interpret the pre-trained architectures before the distillation process, and the architectures resulting from knowledge distillation to ensure that we can maintain performance and that the information learnt by both architectures are similar. It is concluded that is possible to reduce 25x the number of parameters while maintaining good performance and reducing the inference time by 5.32 s. Allied with this, through the interpretability of the models was concluded that both before and after the knowledge distillation are possible to identify ulcers, bleeding situations, and lesions caused by the inflammation of the disease.

show abstract

Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretability

Maurício,

Domingues

2024

Pattern Anal Applic

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Knowledge Distillation of Vision Transformers and Convolutional Networks to Predict Inflammatory Bowel Disease

Cited by 1 publication

References 23 publications

Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretability

Distinguishing between Crohn’s disease and ulcerative colitis using deep learning models with interpretability

Contact Info

Product

Resources

About