sheraz ahmed - Academia.edu (original) (raw)

Papers by sheraz ahmed

Research paper thumbnail of DocXClassifier: Towards a Robust and Interpretable Deep Neural Network for Document Image Classification

Research Square (Research Square), Nov 16, 2023

Research paper thumbnail of Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

arXiv (Cornell University), Nov 26, 2020

Research paper thumbnail of Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN

Research paper thumbnail of Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis

arXiv (Cornell University), Mar 2, 2021

Research paper thumbnail of Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification

arXiv (Cornell University), Jun 13, 2022

Research paper thumbnail of A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets

arXiv (Cornell University), Apr 13, 2022

Research paper thumbnail of Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training

arXiv (Cornell University), Mar 3, 2022

Health mentioning classification (HMC) classifies an input text as health mention or not. Figurat... more Health mentioning classification (HMC) classifies an input text as health mention or not. Figurative and non-health mention of disease words makes the classification task challenging. Learning the context of the input text is the key to this problem. The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results. In this paper, we improve the word representation of the input text using adversarial training that acts as a regularizer during fine-tuning of the model. We generate adversarial examples by perturbing the embeddings of the model and then train the model on a pair of clean and adversarial examples. Additionally, we utilize contrastive loss that pushes a pair of clean and perturbed examples close to each other and other examples away in the representation space. We train and evaluate the method on an extended version of the publicly available PHM2017 dataset. Experiments show an improvement of 1.0% over BERT Large baseline and 0.6% over RoBERTa Large baseline, whereas 5.8% over the state-of-the-art in terms of F1 score. Furthermore, we provide a brief analysis of the results by utilizing the power of explainable AI.

Research paper thumbnail of Self-Supervised Representation Learning for Document Image Classification

Research paper thumbnail of Explaining AI-Based Decision Support Systems Using Concept Localization Maps

Communications in computer and information science, 2020

Research paper thumbnail of Benchmarking Deep Learning Models for Classification of Book Covers

SN computer science, Apr 24, 2020

Research paper thumbnail of A Hybrid Approach and Unified Framework for Bibliographic Reference Extraction

Research paper thumbnail of Analyzing the Potential of Active Learning for Document Image Classification

Deep learning has been extensively researched in the field of document analysis and has shown exc... more Deep learning has been extensively researched in the field of document analysis and has shown excellent performance across a wide range of document-related tasks. As a result, a great deal of emphasis is now being placed on its practical deployment and integration into modern industrial document processing pipelines. It is well-known, however, that deep learning models are data-hungry and often require huge volumes of annotated data in order to achieve competitive performances. And since data annotation is a costly and labor-intensive process, it remains one of the major hurdles to their practical deployment. This study investigates the possibility of using active learning to reduce the costs of data annotation in the context of Document Image Classification, which is one of the core components of modern document processing pipelines. The results of this study demonstrate that by utilizing active learning (AL), deep document classification models can achieve competitive performances...

Research paper thumbnail of Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis

arXiv (Cornell University), Mar 2, 2021

Research paper thumbnail of Learning the micro deformations by max-pooling for offline signature verification

Research paper thumbnail of Towards Privacy Preserved Document Image Classification - A Comprehensive Benchmark

This paper presents a comprehensive benchmarking of privacy preserving techniques for document im... more This paper presents a comprehensive benchmarking of privacy preserving techniques for document image classification.

Research paper thumbnail of Explaining AI-Based Decision Support Systems Using Concept Localization Maps

Communications in Computer and Information Science, 2020

Research paper thumbnail of ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions

Computer Methods and Programs in Biomedicine, 2022

Research paper thumbnail of Confident Classification Using a Hybrid Between Deterministic and Probabilistic Convolutional Neural Networks

IEEE Access, 2020

Traditional neural networks trained using point-based maximum likelihood estimation are determini... more Traditional neural networks trained using point-based maximum likelihood estimation are deterministic models and have exhibited near-human performance in many image classification tasks. However, their insistence on representing network parameters with point-estimates renders them incapable of capturing all possible combinations of the weights; consequently, resulting in a biased predictor towards their initialisation. Most importantly, these deterministic networks are inherently unable to provide any uncertainty estimate for their prediction which is highly sought after in many critical application areas. On the other hand, Bayesian neural networks place a probability distribution on network weights and give a built-in regularisation effect making these models able to learn well from small datasets without overfitting. These networks provide a way of generating posterior distribution which can be used for model's uncertainty estimation. However, Bayesian estimation is computationally very expensive since it greatly widens the parameter space. This paper proposes a hybrid convolutional neural network which combines high accuracy of deterministic models with posterior distribution approximation of Bayesian neural networks. This hybrid architecture is validated on 13 publicly available benchmark classification datasets from a wide range of domains and different modalities like natural scene images, medical images, and time-series. Our results show that the proposed hybrid approach performs better than both deterministic and Bayesian methods in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. We further employ this uncertainty to filter out unconfident predictions and achieve significant additional gain in accuracy for the remaining predictions. INDEX TERMS Bayesian estimation, convolutional neural networks, hybrid neural networks, image classification, time-series classification, uncertainty estimation.

Research paper thumbnail of Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

ArXiv, 2020

Remarkable success of modern image-based AI methods and the resulting interest in their applicati... more Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need for explainable AI does not stem only from ethical and moral grounds but also from stricter legislation around the world mandating clear and justifiable explanations of any decision taken or assisted by AI. Especially in the medical context where Computer-Aided Diagnosis can have a direct influence on the treatment and well-being of patients, transparency is of utmost importance for safe transition from lab research to real world clinical practice. This paper provides a comprehensive overview of current state-of-the-art in explaining and interpreting Deep Learning based algorithms in applications of medical research and diagnosis of diseases. We discuss early achievements in development of explainable AI for validation of known disease criteria, e...

Research paper thumbnail of Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction

ArXiv, 2021

COVID-19 has affected the world economy and the daily life routine of almost everyone. It has bee... more COVID-19 has affected the world economy and the daily life routine of almost everyone. It has been a hot topic on social media platforms such as Twitter, Facebook, etc. These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread. Twitter's retweet functionality allows users to share the existing content with other users without altering the original content. Analysis of social media platforms can help in detecting emergencies during pandemics that lead to taking preventive measures. One such type of analysis is predicting the number of retweets for a given COVID-19 related tweet. Recently, CIKM organized a retweet prediction challenge for COVID-19 tweets focusing on using numeric features only. However, our hypothesis is, tweet text may play a vital role in an accurate retweet prediction. In this paper, we combine numeric and text features for COVID-19 related retweet predictions. For t...

Research paper thumbnail of DocXClassifier: Towards a Robust and Interpretable Deep Neural Network for Document Image Classification

Research Square (Research Square), Nov 16, 2023

Research paper thumbnail of Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

arXiv (Cornell University), Nov 26, 2020

Research paper thumbnail of Probabilistic Forecasting of Sensory Data With Generative Adversarial Networks – ForGAN

Research paper thumbnail of Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis

arXiv (Cornell University), Mar 2, 2021

Research paper thumbnail of Revisiting the Shape-Bias of Deep Learning for Dermoscopic Skin Lesion Classification

arXiv (Cornell University), Jun 13, 2022

Research paper thumbnail of A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets

arXiv (Cornell University), Apr 13, 2022

Research paper thumbnail of Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training

arXiv (Cornell University), Mar 3, 2022

Health mentioning classification (HMC) classifies an input text as health mention or not. Figurat... more Health mentioning classification (HMC) classifies an input text as health mention or not. Figurative and non-health mention of disease words makes the classification task challenging. Learning the context of the input text is the key to this problem. The idea is to learn word representation by its surrounding words and utilize emojis in the text to help improve the classification results. In this paper, we improve the word representation of the input text using adversarial training that acts as a regularizer during fine-tuning of the model. We generate adversarial examples by perturbing the embeddings of the model and then train the model on a pair of clean and adversarial examples. Additionally, we utilize contrastive loss that pushes a pair of clean and perturbed examples close to each other and other examples away in the representation space. We train and evaluate the method on an extended version of the publicly available PHM2017 dataset. Experiments show an improvement of 1.0% over BERT Large baseline and 0.6% over RoBERTa Large baseline, whereas 5.8% over the state-of-the-art in terms of F1 score. Furthermore, we provide a brief analysis of the results by utilizing the power of explainable AI.

Research paper thumbnail of Self-Supervised Representation Learning for Document Image Classification

Research paper thumbnail of Explaining AI-Based Decision Support Systems Using Concept Localization Maps

Communications in computer and information science, 2020

Research paper thumbnail of Benchmarking Deep Learning Models for Classification of Book Covers

SN computer science, Apr 24, 2020

Research paper thumbnail of A Hybrid Approach and Unified Framework for Bibliographic Reference Extraction

Research paper thumbnail of Analyzing the Potential of Active Learning for Document Image Classification

Deep learning has been extensively researched in the field of document analysis and has shown exc... more Deep learning has been extensively researched in the field of document analysis and has shown excellent performance across a wide range of document-related tasks. As a result, a great deal of emphasis is now being placed on its practical deployment and integration into modern industrial document processing pipelines. It is well-known, however, that deep learning models are data-hungry and often require huge volumes of annotated data in order to achieve competitive performances. And since data annotation is a costly and labor-intensive process, it remains one of the major hurdles to their practical deployment. This study investigates the possibility of using active learning to reduce the costs of data annotation in the context of Document Image Classification, which is one of the core components of modern document processing pipelines. The results of this study demonstrate that by utilizing active learning (AL), deep document classification models can achieve competitive performances...

Research paper thumbnail of Deep Learning Based Decision Support for Medicine -- A Case Study on Skin Cancer Diagnosis

arXiv (Cornell University), Mar 2, 2021

Research paper thumbnail of Learning the micro deformations by max-pooling for offline signature verification

Research paper thumbnail of Towards Privacy Preserved Document Image Classification - A Comprehensive Benchmark

This paper presents a comprehensive benchmarking of privacy preserving techniques for document im... more This paper presents a comprehensive benchmarking of privacy preserving techniques for document image classification.

Research paper thumbnail of Explaining AI-Based Decision Support Systems Using Concept Localization Maps

Communications in Computer and Information Science, 2020

Research paper thumbnail of ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions

Computer Methods and Programs in Biomedicine, 2022

Research paper thumbnail of Confident Classification Using a Hybrid Between Deterministic and Probabilistic Convolutional Neural Networks

IEEE Access, 2020

Traditional neural networks trained using point-based maximum likelihood estimation are determini... more Traditional neural networks trained using point-based maximum likelihood estimation are deterministic models and have exhibited near-human performance in many image classification tasks. However, their insistence on representing network parameters with point-estimates renders them incapable of capturing all possible combinations of the weights; consequently, resulting in a biased predictor towards their initialisation. Most importantly, these deterministic networks are inherently unable to provide any uncertainty estimate for their prediction which is highly sought after in many critical application areas. On the other hand, Bayesian neural networks place a probability distribution on network weights and give a built-in regularisation effect making these models able to learn well from small datasets without overfitting. These networks provide a way of generating posterior distribution which can be used for model's uncertainty estimation. However, Bayesian estimation is computationally very expensive since it greatly widens the parameter space. This paper proposes a hybrid convolutional neural network which combines high accuracy of deterministic models with posterior distribution approximation of Bayesian neural networks. This hybrid architecture is validated on 13 publicly available benchmark classification datasets from a wide range of domains and different modalities like natural scene images, medical images, and time-series. Our results show that the proposed hybrid approach performs better than both deterministic and Bayesian methods in terms of classification accuracy and also provides an estimate of uncertainty for every prediction. We further employ this uncertainty to filter out unconfident predictions and achieve significant additional gain in accuracy for the remaining predictions. INDEX TERMS Bayesian estimation, convolutional neural networks, hybrid neural networks, image classification, time-series classification, uncertainty estimation.

Research paper thumbnail of Achievements and Challenges in Explaining Deep Learning based Computer-Aided Diagnosis Systems

ArXiv, 2020

Remarkable success of modern image-based AI methods and the resulting interest in their applicati... more Remarkable success of modern image-based AI methods and the resulting interest in their applications in critical decision-making processes has led to a surge in efforts to make such intelligent systems transparent and explainable. The need for explainable AI does not stem only from ethical and moral grounds but also from stricter legislation around the world mandating clear and justifiable explanations of any decision taken or assisted by AI. Especially in the medical context where Computer-Aided Diagnosis can have a direct influence on the treatment and well-being of patients, transparency is of utmost importance for safe transition from lab research to real world clinical practice. This paper provides a comprehensive overview of current state-of-the-art in explaining and interpreting Deep Learning based algorithms in applications of medical research and diagnosis of diseases. We discuss early achievements in development of explainable AI for validation of known disease criteria, e...

Research paper thumbnail of Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction

ArXiv, 2021

COVID-19 has affected the world economy and the daily life routine of almost everyone. It has bee... more COVID-19 has affected the world economy and the daily life routine of almost everyone. It has been a hot topic on social media platforms such as Twitter, Facebook, etc. These social media platforms enable users to share information with other users who can reshare this information, thus causing this information to spread. Twitter's retweet functionality allows users to share the existing content with other users without altering the original content. Analysis of social media platforms can help in detecting emergencies during pandemics that lead to taking preventive measures. One such type of analysis is predicting the number of retweets for a given COVID-19 related tweet. Recently, CIKM organized a retweet prediction challenge for COVID-19 tweets focusing on using numeric features only. However, our hypothesis is, tweet text may play a vital role in an accurate retweet prediction. In this paper, we combine numeric and text features for COVID-19 related retweet predictions. For t...