HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and UNet for Polyps Segmentation

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Paper: http://ceur-ws.org/Vol-2882/paper20.pdf YouTube: https://youtu.be/CVelQl5Luf0 Quoc-Huy Trinh, Minh-Van Nguyen, Thiet-Gia Huynh and Minh-Triet Tran : HCMUS-Juniors 2020 at Medico Task in MediaEval 2020: Refined Deep Neural Network and UNet for Polyps Segmentation. Proc. of MediaEval 2020, 14-15 December 2020, Online. The Medico: Multimedia Task focuses on developing an efficient and accurate framework to computer-aided diagnosis systems for automatic polyp segmentation to detect all types of polyps in endoscopic images of the gastrointestinal (GI) tract. We are HCMUS-team approach a solution, which includes combination Residual module, Inception module, Adaptive Convolutional neural network with Unet model and PraNet to semantic segmentation all types of polyps in endoscopic images. We submit multiple runs with different architecture and parameters in our model. Our methods show potential results in accuracy and efficiency through multiple experiments.

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Author: Quoc Huy Trinh, Minh Van Nguyen, Thiet Gia Huynh, Minh Triet Tran
HCMUS-Juniors 2020
at Medico Task in MediaEval 2020:
Refined Deep Neural Network and U-Net
for Polyps Segmentation
DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 1

DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 2
MENU
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION

DATE: 10/12/2020 MEDIAEVAL 2020
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ABSTRACT
The Medico: Multimedia Task 2020 focuses on developing an efficient and accurate computer-aided
diagnosis system for automatic segmentation. We participate in task 1, polyps segmentation task, which is
to develop algorithms for segmenting polyps on a comprehensive dataset. In this task, we propose methods
combining Residual module, Inception module, Adaptive Convolutional neural network with U-Net model
and PraNet for semantic segmentation of various types of polyps in endoscopic images. We select 5 runs
with different architecture and parameters in our methods. Our methods show potential results in accuracy
and efficiency through multiple experiments.

DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 4
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION

DATE: 10/12/2020 MEDIAEVAL 2020
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CHALLENGE DESCRIPTION
The “Medico automatic polyp segmentation challenge” aims to develop computer-aided diagnosis systems
for automatic polyp segmentation to detect all types of polyps (for example, irregular polyp, smaller or flat
polyps) with high efficiency and accuracy. The main goal of the challenge is to benchmark semantic
segmentation algorithms on a publicly available dataset, emphasizing robustness, speed, and generalization.
Participants will get access to a dataset consisting of 1,000 segmented polyp images from the
gastrointestinal tract and a separate testing dataset. The challenge consists of two mandatory tasks, each
focused on a different requirement for efficient polyp detection. We hope that this task encourages
multimedia researchers to apply their vast knowledge to the medical field and make an impact that may
affect real live

DATE: 10/12/2020 MEDIAEVAL 2020
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• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION

DATASET
The dataset contains 1,000 polyp images and their corresponding ground truth mask. The datasets were collected
from real routine clinical examinations at Vestre Viken Health Trust (VV) in Norway by expert
gastroenterologists. The VV is the collaboration of the four hospitals that provide healthcare service to 470,000
peoples
DATE: 10/12/2020 MEDIAEVAL 2020
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POLYPS IMAGE POLYPS MASK

DATE: 10/12/2020 MEDIAEVAL 2020
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• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION

DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT 9
METHOD
- U-Net: Using the simple U-Net
- Leaky ReLU + U-Net: Combine Leaky ReLU in convolution block with the U-Net
- Inception Modules + U-Net: Combine Inception modules in convolution block with
the U-Net
- ResUNet: Combine Residual blocks of convolutional layers
- PraNet: a parallel reverse attention network

DATE: 10/12/2020 MEDIAEVAL 2020
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• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION
10

DATE: 10/12/2020 MEDIAEVAL 2020
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RESULT: Our team results after 5 runs
RUN JACCARD DSC RECALL PRECISION ACCURACY F2
1 0.322841318 0.434127614 0.552935567 0.408340977 0.861768135 0.483433644
2 0.290599856 0.41149787 0.765371714 0.329960348 0.739005324 0.524980002
3 0.405763698 0.514761924 0.507163264 0.757444359 0.901111603 0.500731409
4 0.294509379 0.418891573 0.764476695 0.340514156 0.75517951 0.534777353
5 0.765977082 0.840506183 0.894394614 0.844555946 0.946558279 0.857688914
11

DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT
• ABSTRACT
• CHALLENGE INTRODUCTION
• DATASET
• METHOD
• RESULT
• DISSCUSSION
12

DISSCUSSION
DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT
When we use PraNet to test on Medical Images, it performs better than on the camouflage experiment. While PraNet
gets high accuracy, but the architecture requires a well-qualified setting to run the model.
13

DATE: 10/12/2020 MEDIAEVAL 2020
HCMUS-FIT
THANKS FOR WATCHING
14

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Personal Air Quality Index Prediction Using Inverse Distance Weighting Method

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper11.pdf YouTube: https://youtu.be/fBPuacAZkxs Minh-Son Dao, Peijiang Zhao, Thanh Nguyen, Thanh Binh Nguyen, Duc Tien Dang Nguyen and Cathal Gurrin : Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health Lifelog Data Analysis. Proc. of MediaEval 2020, 14-15 December 2020, Online. This paper provides a description of the MediaEval 2020 “Multimodal personal health lifelog data analysis". The purpose of this task is to develop approaches that process the environment data to obtain insights about personal wellbeing. Establishing the association between people’s wellbeing and properties of the surrounding environment which is vital for numerous research. Our task focuses on the internal associations of heterogeneous data. Participants create systems that derive insights from multimodal lifelog data that are important for health and wellbeing to tackle two challenging subtasks. The first task is to investigate whether we can use public/open data to predict personal air pollution data. The second task is to develop approaches to predict personal air quality index(AQI) using images captured by people (plus GAQD). This task targets (but is not limited to) researchers in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, and atmospheric science. Presented by: Peijiang Zhao

Overview of MediaEval 2020 Insights for Wellbeing: Multimodal Personal Health...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper37.pdf YouTube: https://youtu.be/4ROoOzdQzEI Muhammad Hanif, Huzaifa Joozer, Muhammad Atif Tahir and Muhammad Rafi : Ensemble based method for the classification of flooding event using social media data. Proc. of MediaEval 2020, 14-15 December 2020, Online. This paper presents the method proposed and implemented by team FAST-NU-DS, in "The Flood-related Multimedia Task at MediaEval 2020". The task includes data of tweets in Italian language, extracted during floods between 2017 and 2019. The proposed method has utilized text of the tweet and its relevant image for the purpose of binary classification, which identifies whether or not the particular tweet is about flood incident. The proposed method has designed an ensemble based method for the classification of tweets, on the basis of textual data, visual data and combination of both. For visual data, the proposed method has utilized the technique of data augmentation for oversampling of the minority class and applied stratified random sampling for the selection of input. Moreover, Visual Geometry Group (VGG16) convolutional neural network, pretrained on ImageNet and Places365 is utilized by the proposed method. For classification of textual data, the technique of Term Frequency Inverse Document Frequency (TF-IDF) is utilized for feature representation and Multinomial Naive-Bayes classifier is used for the prediction of class. The prediction of image and text are combined for the prediction of each instance. The evaluation of method revealed 36.31%, 20.76% and 27.86% F1-score for text, image and combination of both text and image respectively. Presented by: Muhammad Hanif

Ensemble based method for the classification of flooding event using social m...

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper35.pdf Firoj Alam, Zohaib Hassan, Kashif Ahmad, Asma Gul, Michael Reiglar, Nicola Conci and Ala Al-Fuqaha : Flood Detection via Twitter Streams using Textual and Visual Features. Proc. of MediaEval 2020, 14-15 December 2020, Online. The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over Twitter. In total, we proposed four different solutions including a multi-modal solution combining textual and visual information for the mandatory run, and three single modal image and text-based solutions as optional runs. In the multi-modal method, we rely on a supervised multimodal bitransformer model that combines textual and visual features in an early fusion, achieving a micro F1-score of .859 on the development data set. For the text-based flood events detection, we use a transformer network (i.e., pretrained Italian BERT model) achieving an F1-score of .853. For image-based solutions, we employed multiple deep models, pre-trained on both, the Ima- geNet and places data sets, individually and combined in an early fusion achieving F1-scores of .816 and .805 on the development set, respectively.

Flood Detection via Twitter Streams using Textual and Visual Features

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper34.pdf YouTube: https://youtu.be/3f_Q1WeulbI Naina Said, Kashif Ahmad, Asma Gul, Nasir Ahmad and Ala Al-Fuqaha : Floods Detection in Twitter Text and Images. Proc. of MediaEval 2020, 14-15 December 2020, Online. In this paper, we present our methods for the MediaEval 2020 Flood Related Multimedia task, which aims to analyze and combine textual and visual content from social media for the detection of real-world flooding events. The task mainly focuses on identifying floods related tweets relevant to a specific area. We propose several schemes to address the challenge. For text-based flood events detection, we use three different methods, relying on Bog of Words (BOW) and an Italian Version of Bert individually and in combination, achieving an F1-score of 0.77%, 0.68%, and 0.70% on the development set, respectively. For the visual analysis, we rely on features extracted via multiple state-of-the-art deep models pre-trained on ImageNet. The extracted features are then used to train multiple individual classifiers whose scores are then combined in a late fusion manner achieving an F1-score of 0.75%. For our mandatory multi-modal run, we combine the classification scores obtained with the best textual and visual schemes in a late fusion manner. Overall, better results are obtained with the multimodal scheme achieving an F1-score of 0.80% on the development set Presented by: Naina Said

Floods Detection in Twitter Text and Images

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper19.pdf Rabiul Islam Jony and Alan Woodley : Flood Detection in Twitter Using a Novel Learning Method for Neural Networks. Proc. of MediaEval 2020, 14-15 December 2020, Online. In this paper we use a novel backpropagation technique, Direct Backpropagation (DBP) to train a neural network and use it to detect flooding in Twitter posts. We use the the textual information from the tweets and the visual features form the associated images to classify the posts into two categories, flood (1) and no-flood (0). We also fuse these two modes using fusion methods for the classification. For the classification task we employ a neural network that we train using our proposed method instead of typical backpropgation method. This work has been done in the context of the MediaEval 2020 Flood-Related Multimedia Task.

Flood Detection in Twitter Using a Novel Learning Method for Neural Networks

multimediaeval

Paper: http://ceur-ws.org/Vol-2882/paper5.pdf YouTube: https://youtu.be/s2aVeW3ig0s Stelios Andreadis, Ilias Gialampoukidis, Anastasios Karakostas, Stefanos Vrochidis, Ioannis Kompatsiaris, Roberto Fiorin, Daniele Norbiato and Michele Ferri : The Flood-related Multimedia Task at MediaEval 2020. Proc. of MediaEval 2020, 14-15 December 2020, Online. This paper provides a description of the Flood-related Multimedia Task at MediaEval 2020. The primary goal of the task is to analyse and combine textual and visual content from social media data that reflect real-world events. The focus is on natural disasters and especially on flooding incidents, which are frequent around the globe and have large social consequences for communities and individuals. In particular, the task requires participants to identify Twitter posts that are relevant to flood events in a specific area of interest, based on their text and images. The automatic classification of posts as relevant or not relevant will essentially improve the quality of retrieved social media data, so that they can play a more valuable role in the emergency management. Presented by: Ilias Gialampoukidis

The Flood-related Multimedia Task at MediaEval 2020

multimediaeval

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