37 citations · 76 across the 7 of their papers we have counts for
8 papers
Channel Attention Separable Convolution Network for Skin Lesion Segmentation
Changlu Guo, Jiangyan Dai, Marton Szemenyei +1
Skin cancer is a frequently occurring cancer in the human population, and it is very important to be able to diagnose malignant tumors in the body early. Lesion segmentation is cru…
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer
Zoltán Lőrincz, Márton Szemenyei, Róbert Moni
Imitation Learning uses the demonstrations of an expert to uncover the optimal policy and it is suitable for real-world robotics tasks as well. In this case, however, the training…
Residual Spatial Attention Network for Retinal Vessel Segmentation
Changlu Guo, Márton Szemenyei, Yugen Yi +2
Reliable segmentation of retinal vessels can be employed as a way of monitoring and diagnosing certain diseases, such as diabetes and hypertension, as they affect the retinal vascu…
Dense Residual Network for Retinal Vessel Segmentation
Changlu Guo, Márton Szemenyei, Yugen Yi +3
Retinal vessel segmentation plays an imaportant role in the field of retinal image analysis because changes in retinal vascular structure can aid in the diagnosis of diseases such…
Channel Attention Residual U-Net for Retinal Vessel Segmentation
Changlu Guo, Márton Szemenyei, Yangtao Hu +3
Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Resid…
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation
Changlu Guo, Márton Szemenyei, Yugen Yi +3
The precise segmentation of retinal blood vessels is of great significance for early diagnosis of eye-related diseases such as diabetes and hypertension. In this work, we propose a…
ROBO: Robust, Fully Neural Object Detection for Robot Soccer
Marton Szemenyei, Vladimir Estivill-Castro
Deep Learning has become exceptionally popular in the last few years due to its success in computer vision and other fields of AI. However, deep neural networks are computationally…
Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning
Patrik Reizinger, Márton Szemenyei
Reinforcement Learning enables to train an agent via interaction with the environment. However, in the majority of real-world scenarios, the extrinsic feedback is sparse or not suf…