activity
20182026
most citedSinGAN-Seg: Synthetic training data generation for medical image segmentation

126 citations · 308 across the 24 of their papers we have counts for

collaborators
Showing eess.IVShow all

8 papers · 1 filter

eess.IV2026

Anatomy-Preserving Latent Diffusion for Generation of Brain Segmentation Masks with Ischemic Infarct

Lucia Borrego, Vajira Thambawita, Marco Ciuffreda +3

The scarcity of high-quality segmentation masks remains a major bottleneck for medical image analysis, particularly in non-contrast CT (NCCT) neuroimaging, where manual annotation…

eess.IV2023

Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges

Debesh Jha, Vanshali Sharma, Debapriya Banik +30

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps durin…

eess.IV2023★ 1 cited

Mask-conditioned latent diffusion for generating gastrointestinal polyp images

Roman Macháček, Leila Mozaffari, Zahra Sepasdar +4

In order to take advantage of AI solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in…

eess.IV2022

MLC at HECKTOR 2022: The Effect and Importance of Training Data when Analyzing Cases of Head and Neck Tumors using Machine Learning

Vajira Thambawita, Andrea M. Storås, Steven A. Hicks +2

Head and neck cancers are the fifth most common cancer worldwide, and recently, analysis of Positron Emission Tomography (PET) and Computed Tomography (CT) images has been proposed…

eess.IV2022

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps

Jan Andre Fagereng, Vajira Thambawita, Andrea M. Storås +4

Early identification of a polyp in the lower gastrointestinal (GI) tract can lead to prevention of life-threatening colorectal cancer. Developing computer-aided diagnosis (CAD) sys…

eess.IV2021★ 126 cited

SinGAN-Seg: Synthetic training data generation for medical image segmentation

Vajira Thambawita, Pegah Salehi, Sajad Amouei Sheshkal +6

Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Artificial…

eess.IV2021★ 3 cited

DivergentNets: Medical Image Segmentation by Network Ensemble

Vajira Thambawita, Steven A. Hicks, Pål Halvorsen +1

Detection of colon polyps has become a trending topic in the intersecting fields of machine learning and gastrointestinal endoscopy. The focus has mainly been on per-frame classifi…

eess.IV2019★ 4 cited

Stacked dense optical flows and dropout layers to predict sperm motility and morphology

Vajira Thambawita, Pål Halvorsen, Hugo Hammer +2

In this paper, we analyse two deep learning methods to predict sperm motility and sperm morphology from sperm videos. We use two different inputs: stacked pure frames of videos and…