1 citations · 2 across the 7 of their papers we have counts for
7 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…
Size Aware Cross-shape Scribble Supervision for Medical Image Segmentation
Jing Yuan, Tania Stathaki
Scribble supervision, a common form of weakly supervised learning, involves annotating pixels using hand-drawn curve lines, which helps reduce the cost of manual labelling. This te…
Detecting and Triaging Spoofing using Temporal Convolutional Networks
Kaushalya Kularatnam, Tania Stathaki
As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient market…
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…
Adaptive manifold for imbalanced transductive few-shot learning
Michalis Lazarou, Yannis Avrithis, Tania Stathaki
Transductive few-shot learning algorithms have showed substantially superior performance over their inductive counterparts by leveraging the unlabeled queries. However, the vast ma…