Slovakia

ophthalmology

optic disc and vessel segmentation

morphological operations

frst

attention u-net


IWSSIP is an international conference that brings together researchers and developers from both academia and industry reporting the latest scientific and theoretical advances to discuss and debate major issues and state-of-the-art aspects on systems, signals and image processing.

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IWSSIP 2021

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020

"The main aim of this article is to compare U-Net models
with different methods of medical image segmentation in ophthalmology when searching for the optic disc and vessels on the retina. We
used freely available databases to analyze the results. DRIVE, STARE
and MESSIDOR were used for blood vessel segmentation and DRION
database was used for optic disc segmentation. In vascular segmentation,
we compared the method based on morphological operations with U-Net
convolutional network. The best results were obtained by neural network, where we achieved accuracy of 96.7% on DRIVE dataset. Methods
based on morphological operations, Attentional U-Net network and Fast
Radial Symmetry Transform were used for optic disc localization, where
the U-net network achieved the best sensitivity of 91.6%. We show that
in our tests U-Net architectures achieve better results in vessel and optic disc segmentation even compared to standard methods, that do not
depend on the amount of available data."

Segmentation of signiﬁcant regions in retinal images: perspective of U-Net network through a comparative approach

technical paper

Comparison of TCP Receiver-based Loss Differentiation Algorithms in Wired/Wireless Networks

Sensitivity Analysis for SDN Based Wi-Fi Handover Delay Model

Classification of Toxic Ornamental Plants for Domestic Animals using CNN

Clustering Thermography Data to Follow up Breast Cancer Treatment

Automatic recognition of Native advertisements for the Slovak language

CNN based speech commands recognition

Sudden Death Classification Using Electrocardiogram Signals and Structural Co-occurrence Matrix in Patients with Chagas Disease

An Approach for BCI using Motor Imagery Based on Wavelet Transform, and Convolutional Neural Network

Fingerprint Classification based on the Henry System via ResNet

Multi-tool environment with an online assistant for teaching Direct Digital Synthesis

https://d3smihljt9218e.cloudfront.net/transcript/14017/subtitle/c95c21c8b7d2f3964f841dd7ef2fe440.vtt

English (automatic)

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Segmentation of signiﬁcant regions in retinal images: perspective of U-Net network through a comparative approach

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Comparison of TCP Receiver-based Loss Differentiation Algorithms in Wired/Wireless Networks

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