2 citations · 6 across the 10 of their papers we have counts for
11 papers
Towards Data-Efficient Medical Imaging: A Generative and Semi-Supervised Framework
Mosong Ma, Tania Stathaki, Michalis Lazarou
Deep learning in medical imaging is often limited by scarce and imbalanced annotated data. We present SSGNet, a unified framework that combines class specific generative modeling w…
Enhanced Detection of Tiny Objects in Aerial Images
Kihyun Kim, Michalis Lazarou, Tania Stathaki
While one-stage detectors like YOLOv8 offer fast training speed, they often under-perform on detecting small objects as a trade-off. This becomes even more critical when detecting…
No Masks Needed: Explainable AI for Deriving Segmentation from Classification
Mosong Ma, Tania Stathaki, Michalis Lazarou
Medical image segmentation is vital for modern healthcare and is a key element of computer-aided diagnosis. While recent advancements in computer vision have explored unsupervised…
Image compositing is all you need for data augmentation
Ang Jia Ning Shermaine, Michalis Lazarou, Tania Stathaki
This paper investigates the impact of various data augmentation techniques on the performance of object detection models. Specifically, we explore classical augmentation methods, i…
Image edge enhancement for effective image classification
Tianhao Bu, Michalis Lazarou, Tania Stathaki
Image classification has been a popular task due to its feasibility in real-world applications. Training neural networks by feeding them RGB images has demonstrated success over it…
Adaptive Anchor Label Propagation for Transductive Few-Shot Learning
Michalis Lazarou, Yannis Avrithis, Guangyu Ren +1
Few-shot learning addresses the issue of classifying images using limited labeled data. Exploiting unlabeled data through the use of transductive inference methods such as label pr…