Semantic Segmentation of Herbarium Specimens Using Deep Learning Techniques

2020 ◽  
pp. 321-330
Author(s):  
Burhan Rashid Hussein ◽  
Owais Ahmed Malik ◽  
Wee-Hong Ong ◽  
Johan Willem Frederik Slik
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Herbarium Specimens ◽  
Learning Techniques

scholarly journals A Medical Service Application Based on 3D-CNN and Knowledge Graph

2021 ◽  
Vol 2078 (1) ◽  
pp. 012048
Author(s):  
Jiasheng Ni
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Cancer Detection ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Knowledge Graph ◽  
Medical Response ◽  
Learning Techniques ◽  
3D Cnn ◽  
Lung Cancer Detection

Abstract Remote medical prognosis application is a category of medical tests tool designed to collect patients’ body conditions and offer diagnosis results synchronously. However, most online applications are predicated on a simple chat bot which typically redirect patients to other online medical websites, which undermines the user experience and may prompt useless information for their reference. To tackle these issues, this paper proposed a medical prognosis application with deep learning techniques for a more responsive and intelligent medical prognosis procedure. This application can be break down into three parts-lung cancer detection, a database-supporting medical QA bot and a Hierarchical Bidirectional LSTM model (HBDA). A 3D-CNN model is built for the lung cancer detection, with a sequence of sliced CT images as inputs and outputs a probability scaler for tumor indications. A knowledge graph is applied in the medical QA bot implementation and the HBDA model is designed for semantic segmentation in order to better capture users’ intention in medical questions. For the performance of the medical prognosis, since we have limited computer memory, the 3D-CNN didn’t perform very well on detecting tumor conditions in the CT images with accuracy at around 70%. The knowledge graph-based medical QA bot intelligently recognize the underlying pattern in patients’ question and delivered decent medical response. The HBDA model performs well on distinguish the similarities and disparities between various medical questions, reaching accuracy at 90%. These results shed light for the feasibility of utilizing deep learning techniques such as 3D-CNN, Knowledge Graph, and Hierarchical Bi-directional LSTM to simulate the medical prognosis process.

scholarly journals Survey on semantic segmentation using deep learning techniques

2019 ◽  
Vol 338 ◽  
pp. 321-348 ◽  
Author(s):  
Fahad Lateef ◽  
Yassine Ruichek
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Learning Techniques

Text Separation From Document Images

2020 ◽  
pp. 283-313
Author(s):  
Priti P. Rege ◽  
Shaheera Akhter
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Semantic Segmentation ◽  
Document Image ◽  
Training Data ◽  
Document Images ◽  
Learning Techniques ◽  
Extraction Processes ◽  
Segmentation Image

Text separation in document image analysis is an important preprocessing step before executing an optical character recognition (OCR) task. It is necessary to improve the accuracy of an OCR system. Traditionally, for separating text from a document, different feature extraction processes have been used that require handcrafting of the features. However, deep learning-based methods are excellent feature extractors that learn features from the training data automatically. Deep learning gives state-of-the-art results on various computer vision, image classification, segmentation, image captioning, object detection, and recognition tasks. This chapter compares various traditional as well as deep-learning techniques and uses a semantic segmentation method for separating text from Devanagari document images using U-Net and ResU-Net models. These models are further fine-tuned for transfer learning to get more precise results. The final results show that deep learning methods give more accurate results compared with conventional methods of image processing for Devanagari text extraction.

scholarly journals Bayesian U-Net: Estimating Uncertainty in Semantic Segmentation of Earth Observation Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3836
Author(s):  
Clément Dechesne ◽  
Pierre Lassalle ◽  
Sébastien Lefèvre
Keyword(s):  
Deep Learning ◽  
Satellite Images ◽  
State Of The Art ◽  
Qualitative Evaluation ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Earth Observation ◽  
Current State ◽  
Learning Techniques ◽  
Reference Databases

In recent years, numerous deep learning techniques have been proposed to tackle the semantic segmentation of aerial and satellite images, increase trust in the leaderboards of main scientific contests and represent the current state-of-the-art. Nevertheless, despite their promising results, these state-of-the-art techniques are still unable to provide results with the level of accuracy sought in real applications, i.e., in operational settings. Thus, it is mandatory to qualify these segmentation results and estimate the uncertainty brought about by a deep network. In this work, we address uncertainty estimations in semantic segmentation. To do this, we relied on a Bayesian deep learning method, based on Monte Carlo Dropout, which allows us to derive uncertainty metrics along with the semantic segmentation. Built on the most widespread U-Net architecture, our model achieves semantic segmentation with high accuracy on several state-of-the-art datasets. More importantly, uncertainty maps are also derived from our model. While they allow for the performance of a sounder qualitative evaluation of the segmentation results, they also include valuable information to improve the reference databases.

scholarly journals Encoder-Decoder Architecture for Ultrasound IMC Segmentation and cIMT Measurement

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6839
Author(s):  
Aisha Al-Mohannadi ◽  
Somaya Al-Maadeed ◽  
Omar Elharrouss ◽  
Kishor Kumar Sadasivuni
Keyword(s):  
Deep Learning ◽  
Early Diagnosis ◽  
Large Scale ◽  
Intima Media Thickness ◽  
Semantic Segmentation ◽  
Learning Techniques ◽  
Proposed Model ◽  
Decoder Architecture ◽  
Huge Impact ◽  
Good Learning

Cardiovascular diseases (CVDs) have shown a huge impact on the number of deaths in the world. Thus, common carotid artery (CCA) segmentation and intima-media thickness (IMT) measurements have been significantly implemented to perform early diagnosis of CVDs by analyzing IMT features. Using computer vision algorithms on CCA images is not widely used for this type of diagnosis, due to the complexity and the lack of dataset to do it. The advancement of deep learning techniques has made accurate early diagnosis from images possible. In this paper, a deep-learning-based approach is proposed to apply semantic segmentation for intima-media complex (IMC) and to calculate the cIMT measurement. In order to overcome the lack of large-scale datasets, an encoder-decoder-based model is proposed using multi-image inputs that can help achieve good learning for the model using different features. The obtained results were evaluated using different image segmentation metrics which demonstrate the effectiveness of the proposed architecture. In addition, IMT thickness is computed, and the experiment showed that the proposed model is robust and fully automated compared to the state-of-the-art work.

scholarly journals Semantic Segmentation of Underwater Imagery Using Deep Networks Trained on Synthetic Imagery

2018 ◽  
Vol 6 (3) ◽  
pp. 93 ◽  
Author(s):  
Michael O’Byrne ◽  
Vikram Pakrashi ◽  
Franck Schoefs ◽  
and Bidisha Ghosh
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Support Vector ◽  
Natural Scenes ◽  
Underwater Imaging ◽  
Learning Techniques ◽  
Initial Segmentation ◽  
Model Training

Recent breakthroughs in the computer vision community have led to the emergence of efficient deep learning techniques for end-to-end segmentation of natural scenes. Underwater imaging stands to gain from these advances, however, deep learning methods require large annotated datasets for model training and these are typically unavailable for underwater imaging applications. This paper proposes the use of photorealistic synthetic imagery for training deep models that can be applied to interpret real-world underwater imagery. To demonstrate this concept, we look at the specific problem of biofouling detection on marine structures. A contemporary deep encoder–decoder network, termed SegNet, is trained using 2500 annotated synthetic images of size 960 × 540 pixels. The images were rendered in a virtual underwater environment under a wide variety of conditions and feature biofouling of various size, shape, and colour. Each rendered image has a corresponding ground truth per-pixel label map. Once trained on the synthetic imagery, SegNet is applied to segment new real-world images. The initial segmentation is refined using an iterative support vector machine (SVM) based post-processing algorithm. The proposed approach achieves a mean Intersection over Union (IoU) of 87% and a mean accuracy of 94% when tested on 32 frames extracted from two distinct real-world subsea inspection videos. Inference takes several seconds for a typical image.

scholarly journals Digital Naturalist Using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 2061-2065
Author(s):  
Aparna .
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Object Detection ◽  
Wild Species ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Semantic Segmentation ◽  
Model Image ◽  
Image Sensing ◽  
Learning Techniques

A naturalist is someone who studies the patterns of nature identify different kingdom of flora and fauna in the nature. Being able to identify the flora and fauna around us often leads to an interest in protecting wild species, collecting and sharing information about the species we see on our travels is very useful for conserving groups like NCC. Deep-learning based techniques and methods are becoming popular in digital naturalist studies, as their performance is superior in image analysis fields, such as object detection, image classification, and semantic segmentation. Deep-learning techniques have achieved state of-the -art performance for automatic segmentation of digital naturalist through multi-model image sensing. Our task as naturalist has grown widely in the field of natural-historians. It has increased from identification to saviours as well. Not only identifying flora and fauna but also to know about their habits, habitats, living and grouping lead to fetching services for protection as well.

scholarly journals Multiclass semantic segmentation for digitisation of movable heritage using deep learning techniques

2021 ◽  
Vol 12 (25) ◽  
pp. 85
Author(s):  
Giacomo Patrucco ◽  
Francesco Setragno
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Semantic Classification ◽  
Object Categories ◽  
Network Training ◽  
Acquisition Strategies ◽  
Learning Techniques ◽  
High Degree

<p class="VARAbstract">Digitisation processes of movable heritage are becoming increasingly popular to document the artworks stored in our museums. A growing number of strategies for the three-dimensional (3D) acquisition and modelling of these invaluable assets have been developed in the last few years. Their objective is to efficiently respond to this documentation need and contribute to deepening the knowledge of the masterpieces investigated constantly by researchers operating in many fieldworks. Nowadays, one of the most effective solutions is represented by the development of image-based techniques, usually connected to a Structure-from-Motion (SfM) photogrammetric approach. However, while images acquisition is relatively rapid, the processes connected to data processing are very time-consuming and require the operator’s substantial manual involvement. Developing deep learning-based strategies can be an effective solution to enhance the automatism level. In this research, which has been carried out in the framework of the digitisation of a wooden maquettes collection stored in the ‘Museo Egizio di Torino’, using a photogrammetric approach, an automatic masking strategy using deep learning techniques is proposed, to increase the level of automatism and therefore, optimise the photogrammetric pipeline. Starting from a manually annotated dataset, a neural network was trained to automatically perform a semantic classification to isolate the maquettes from the background. The proposed methodology allowed the researchers to obtain automatically segmented masks with a high degree of accuracy. The workflow is described (as regards acquisition strategies, dataset processing, and neural network training). In addition, the accuracy of the results is evaluated and discussed. Finally, the researchers proposed the possibility of performing a multiclass segmentation on the digital images to recognise different object categories in the images, as well as to define a semantic hierarchy to perform automatic classification of different elements in the acquired images.</p><p><strong>Highlights:</strong></p><ul><li><p>In the framework of movable heritage digitisation processes, many procedures are very time-consuming, and they still require the operator’s substantial manual involvement.</p></li><li><p>This research proposes using deep learning techniques to enhance the automatism level in the generation of exclusion masks, improving the optimisation of the photogrammetric procedures.</p></li><li><p>Following this strategy, the possibility of performing a multiclass semantic segmentation (on the 2D images and, consequently, on the 3D point cloud) is also discussed, considering the accuracy of the obtainable results.</p></li></ul>

Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans

2021 ◽  
Author(s):  
Abhishek Shivdeo ◽  
Rohit Lokwani ◽  
Viraj Kulkarni ◽  
Amit Kharat ◽  
Aniruddha Pant
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Ct Scans ◽  
Learning Techniques

scholarly journals A survey on deep learning techniques for image and video semantic segmentation

2018 ◽  
Vol 70 ◽  
pp. 41-65 ◽  
Author(s):  
Alberto Garcia-Garcia ◽  
Sergio Orts-Escolano ◽  
Sergiu Oprea ◽  
Victor Villena-Martinez ◽  
Pablo Martinez-Gonzalez ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Learning Techniques
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