5th international conference on Pattern Recognition and Image Analysis (2021) at University of Kashan

Workshops

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Segmentation of Abnormal Tissues in Medical Images Using Deep Neural Networks
Call for Workshop: Segmentation of Abnormal Tissues in Medical Images Using Deep Neural Networks

Time & Schedule Tuesday, April 27, 2021 Starting from 09:00 till 16:00 No. seats: 30 people 


Abstract
Image Segmentation is a problem in the context of machine vision which is referred as assigning a label to pixel groups (such as abnormalities of medical images) that have common intensity or visual properties. Segmentation of abnormal tissues in medical images is mandatory for locating tumors and pathologies, detecting cancerous regions, and monitoring disease progression. As abnormalities are inherently complex structures, automated segmentation of them is a challenging task. Therefore, more sophisticated techniques are required for accurate modeling and segmentation.
 
Machine learning as one of the main fields of artificial intelligence uses interdisciplinary techniques to build automated systems. These automated systems are able to process and analyze massive amounts of data to forecast and making decisions without human intervention. Deep Learning (DL) techniques as the latest advances in the field of machine learning are able to perform the task of abnormal tissue segmentation accurately at the clinical level. DL techniques are successful in this task as they generate different levels of abstractions and representations through their deep structures. 
 
 

 
Topics



    • Introduction

        • Medical Imaging Processing

        • Medical Image Segmentation

        • Abnormal Tissue Segmentation

        • Challenges of Abnormal Tissue Segmentation

        • Deep Learning



    • Convolutional Neural Networks

    • CNN Architectures for Segmentation

        • Fully Convolutional Networks

        • Deconvolutional Networks

        • U Shaped Network



    • The pipeline of a Typical Deep Method for Segmentation of Abnormal Tissues

    • Considered Applications:

        • Segmentation of Skin Lesions

        • Segmentation of Breast Abnormalities

        • Segmentation of Brain Tumors

        • Segmentation of Covid-19 Abnormalities in Infected Lungs



    • Latest Deep Neural Network Architectural Advances for Segmentation of Abnormal Tissues




 
About the Workshop
This workshop aims to introduce learners to the main DL segmentation architectures. Moreover, the learners will be able to understand underlying theoretical concepts as well as applying the learned concepts and architectures to different abnormalities in medical images.  
 
 
Time & Schedule
Tuesday, 27 April 2021
Starting from 09:00 till 16:00
No. seats: 30 people 
 
 
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Metaheuristic Algorithms: Theory and Implementation
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Time & Schedule Friday, April 30, 2021 Starting from 09:00 till 16:00 No. seats: 30 people 


Abstract
Metaheuristic Algorithms is a sub-field of computational intelligence which aims to solve optimisation problems by modelling a population of agents and co-operative interaction among them. Various metaheuristic algorithms have been developed to solve complex optimisation problems. Examples of such algorithms are particle swarm
optimisation (PSO) , differential evolution (DE) , artificial bee colony algorithm (ABC) , colonial competitive algorithm (CCA) , human mental search( HMS) , and cuckoo search (CS) , among many others.
This workshop tries to cover the general structure of metaheuristic algorithms as well as introduce some of the algorithms and its implementation in Matlab.
 
Main Topics:
Introduction
Common concepts for metaheuristic algorithms
Single-solution based metaheuristic algorithms
Population-based metaheuristic algorithms
Simulated Annealing (theory and implementation in Matlab)
Particle swarm optimisation (theory and implementation in Matlab)
Differential evolution (theory and implementation in Matlab)
Human mental search (theory and implementation in Matlab)
 
Time & Schedule
……..
Starting from 09:00 till 16:00
No. seats: 30 people 
 
Registration Cost
Domestic:
400 Thousand Toman for regular participants
300 Thousand Toman for ISMVIP members/IEEE members/ IPRIA 2021 registered authors
200 Thousand Toman for students/ISMVIP members/IEEE members/ IPRIA 2021 registered authors
International:
75 Euro for regular participants
50 Euro for students/ISMVIP members/IEEE members

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