A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial

Jadon, Shruti; Leary, Owen P.; Pan, Ian; Harder, Tyler; Wright, David W.; Merck, Lisa H.; Merck, Derek

doi:10.1117/12.2566332

Cited by 27 publications

(15 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) Using SSM-Net we can achieve 94.3% accuracy in Sugarcane disease identification even with less amount of data. We envision that the proposed workflow might be applicable to other datasets [6] which we will explore in future. Our code is publicly available on github repository: https://github.com/ shruti-jadon/Plants Disease Detection.…”

Section: Discussionmentioning

confidence: 99%

SSM-Net for Plants Disease Identification in Low Data Regime

Jadon¹

2020

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)

Self Cite

View full text Add to dashboard Cite

Plant disease detection is a necessary step in increasing agricultural production. Due to the difficulty of disease detection, farmers spray every form of pesticide on their crops to save them, causing harm to crop growth and food standards. Deep learning can help a lot in detecting such diseases. However, it is highly inconvenient to collect a large amount of data on all forms of disease of a specific plant species. In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components to solve the problem of disease detection in low data regimes. We showcase that using the SSM net (stacked siamese matching) method, we were able to achieve better decision boundaries and accuracy of 94.3%, an increase of 5% from using the traditional transfer learning approach (VGG16 and Xception net) and 3% from using original matching networks. Furthermore, we were able to attain an F1 score of 0.90 using SSM Net, an improvement from 0.30 using transfer learning and 0.80 using original matching networks. The code is available on Github: https://github.com/shruti-jadon/Plants Disease Detection.

show abstract

Section: Discussionmentioning

confidence: 99%

SSM-Net for Plants Disease Identification in Low Data Regime

Jadon¹

2020

2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Image Augmentation methods have evolved in recent years from simple geometric transformations, color space augmentations to generative adversarial networks, neural style transfer methods. At present, a lot of research is being conducted in application of augmentation methods based on GANs for their practical use-case in medical field community [2]. In addition to general augmentation techniques, use of Data Augmentation can also improve the performance of models and expand limited data-sets to take advantage of the capabilities of big data.…”

Section: Data Augmentation Methodsmentioning

confidence: 99%

An Overview of Deep Learning Architectures in Few-Shot Learning Domain

Jadon¹,

Jadon²

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Since 2012, Deep learning has revolutionized Artificial Intelligence and has achieved state-of-the-art outcomes in different domains, ranging from Image Classification to Speech Generation. Though it has many potentials, our current architectures come with the pre-requisite of large amounts of data. Few-Shot Learning (also known as one-shot learning) is a subfield of machine learning that aims to create such models that can learn the desired objective with less data, similar to how humans learn. In this paper, we have reviewed some of the well-known deep learning-based approaches towards few-shot learning. We have discussed the recent achievements, challenges, and possibilities of improvement of few-shot learning based deep learning architectures. Our aim for this paper is threefold: (i) Give a brief introduction to deep learning architectures for few-shot learning with pointers to core references. (ii) Indicate how deep learning has been applied to the low-data regime, from data preparation to model training. and, (iii) Provide a starting point for people interested in experimenting and perhaps contributing to the field of few-shot learning by pointing out some useful resources and open-source code. Our code is available at Github:https: //github.com/shruti-jadon/Hands-on-One-Shot-Learning

show abstract

“…This double-step configuration relies on the Binary Cross-Entropy (BCE) loss function on the first stage and on the Mean Squared Error (MSE) loss on the second stage to estimate the final severity level. It is a straightforward fact that a proper choice of model and loss functions can significantly improve the performance of the models, as shown in [52,53]. For this reason, the goal of this work is to validate and extend the results in [54], testing the proposed models to a widely extended set of images, and introducing a multi-channel interpretability analysis by exploiting both domain knowledge and a newly added attention-based mechanism.…”

Section: Related Workmentioning

confidence: 98%

Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

et al. 2021

View full text Add to dashboard Cite

Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus, to limit their impact and plan the restoration process, a rapid intervention by authorities is needed, which can be enhanced by the use of satellite imagery and automatic burned area delineation methodologies, accelerating the response and the decision-making processes. In this context, we analyze the burned area severity estimation problem by exploiting a state-of-the-art deep learning framework. Experimental results compare different model architectures and loss functions on a very large real-world Sentinel2 satellite dataset. Furthermore, a novel multi-channel attention-based analysis is presented to uncover the prediction behaviour and provide model interpretability. A perturbation mechanism is applied to an attention-based DS-UNet to evaluate the contribution of different domain-driven groups of channels to the severity estimation problem.

show abstract

A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial

Cited by 27 publications

References 32 publications

SSM-Net for Plants Disease Identification in Low Data Regime

SSM-Net for Plants Disease Identification in Low Data Regime

An Overview of Deep Learning Architectures in Few-Shot Learning Domain

Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction

Contact Info

Product

Resources

About