Rahul Paul- PhD
- PostDoctoral Fellow at Massachusetts General Hospital (Harvard Medical School)
Rahul Paul
- PhD
- PostDoctoral Fellow at Massachusetts General Hospital (Harvard Medical School)
About
61
Publications
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Introduction
Rahul Paul currently works at the Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, as a Postdoctoral Research Fellow. Previously he has worked on various projects on biomedical applications. His current research is focused on using machine learning and deep learning tools to detect early diagnosis, prediction, and prognosis for cancer using different imaging modality, clinical data, and genomics.
Current institution
Massachusetts General Hospital (Harvard Medical School)
Current position
- PostDoctoral Fellow
Publications
Publications (61)
As one of the most powerful natural catastrophes, floods pose serious risks to people’s lives, the integrity of infrastructure, and agricultural landscapes, which increases the toll they take on the economy and society. As a result, it becomes essential to continuously monitor these areas of vulnerability in order to support effective disaster resp...
![Figure 1: Deep neural network architecture [14].](publication/371364256/figure/fig1/AS:11431281166104649@1686157096198/Deep-neural-network-architecture-14_Q320.jpg)
This study explores the potential of the VGG-16 architecture, a Convolutional Neural Network (CNN) model, for accurate brain tumor detection through deep learning. Utilizing a dataset consisting of 1655 brain MRI images with tumors and 1598 images with- out tumors, the VGG-16 model was fine-tuned and trained on this data. Initial training achieved...
The COVID-19 Pandemic has caused unprecedented global challenges, including significant socioeconomic disruptions and the closure of schools and universities in almost all countries. Understanding the role of culture in shaping individual and societal responses to the Pandemic is crucial. This review article examines the applicability of Hofstede's...
The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using Ima...
Background and objectives:
Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures.
Materials and methods:
to reduce noise from medical images, the hybrid probabilistic wi...
Background:
Wearable device technology has recently been involved in the healthcare industry substantially. India is the world's third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable hea...
Purpose/Objective(s)
Brain fogging, which is characterized by memory decline, word-finding difficulty, and decreased multi-tasking ability, is common in head and neck cancer survivors. The precise causes of brain fog are poorly understood, as MRIs are essentially normal in these patients. The purpose of this study was to use artificial intelligence...
Purpose/Objective(s)
Thyroid cancer is one of the most rapidly increasing cancer in the US, largely due to increased detection. BRAF mutation (V600E) is common in thyroid cancer and is a druggable mutation. The purpose of this study was to establish a multimodal artificial intelligence (AI) ultrasound platform that consists of radiomics, topologica...
Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to elimina...
Computed tomography (CT) is used in medical applications to produce digital medical
imaging of the human body and is acquired by the reconstruction process, where X-rays are the key
component of CT imaging. The present coronavirus outbreak has spawned new medical device and
technology research fields. COVID-19 most severely affects people with poor...
Artificial intelligence is used in predicting the clinical outcomes before minimally invasive treatments for benign prostatic hyperplasia, to address the insufficient reliability despite multiple assessment parameters, such as flow rates and symptom scores. Various models of artificial intelligence and its contemporary applications in benign prosta...
Purpose/Objective(s)
Radiation and chemotherapy are highly effective treatments for patients with nasopharyngeal carcinoma (NPC), with a cure rate of 80% or higher. Long-term survivors, despite having normal MRIs, commonly suffer from memory impairment and neurocognitive dysfunction. The purpose of the study was to quantify the long-term impact of...
Purpose/Objective(s)
Thyroid cancer is one of the most rapidly increasing cancer in the US, largely due to increased detection. BRAF mutation (V600E) is common in papillary thyroid carcinoma (PTC) and is associated with poor prognosis. The purpose of this study was to establish a multimodal artificial intelligence (AI) ultrasound platform that cons...
The legal and ethical issues that confront society due to Artificial Intelligence (AI) include privacy and surveillance, bias or discrimination, and potentially the philosophical challenge is the role of human judgment. Concerns about newer digital technologies becoming a new source of inaccuracy and data breaches have arisen as a result of its use...
With the inter and multi-disciplinary collaboration of the medical community with technologists in conjunction with a disproportionately alarming doctor-patient ratio, it has now become a matter of concern for researchers to enhance patient care with advanced technology along with the reduction of burden on medical professionals. Artificial Intelli...
Purpose/Objective(s)
Deintensification for early-stage oropharyngeal carcinoma includes surgery +/- adjuvant radiation therapy without the use of concurrent chemotherapy. Pathologic findings of nodal extracapsular extension (ECE), however, obligate the use of postoperative concurrent chemotherapy in these patients. The purpose of this study was to...
Radiomics is a procedure to access quantitative features from medical images for predictive analysis. Radiomics is used extensively for malignancy and survival analysis, genomic expression analysis, cancer progression, and assessment. But radiomics does not obtain the connected components, loops, or voids information from a region of interest. Topo...
Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, an...
Data science is an interdisciplinary field that extracts knowledge and insights from many structural and unstructured data, using scientific methods, data mining techniques, machine-learning algorithms, and big data. The healthcare industry generates large datasets of useful information on patient demography, treatment plans, results of medical exa...
A number of recent papers have shown experimental evidence that suggests it is possible to build highly accurate deep neural network models to detect COVID-19 from chest X-ray images. In this paper, we show that good generalization to unseen sources has not been achieved. Experiments with richer data sets than have previously been used show models...
Recent advances in artificial intelligence (AI) have certainly had a significant impact on the healthcare industry. In urology, AI has been widely adopted to deal with numerous disorders, irrespective of their severity, extending from conditions such as benign prostate hyperplasia to critical illnesses such as urothelial and prostate cancer. In thi...
Objective:
To develop a decision support system (DSS) for the prediction of the postoperative outcome of a kidney stone treatment procedure, particularly percutaneous nephrolithotomy (PCNL) to serve as a promising tool to provide counseling before an operation.
Materials and methods:
The overall procedure includes data collection and prediction...
Identifying who is infected with the Covid-19 virus is critical for controlling its spread. X-ray machines are widely available worldwide and can quickly provide images that can be used for diagnosis. A number of recent studies claim it may be possible to build highly accurate models, using deep learning, to detect Covid-19 from chest X-ray images....
Acute-on-chronic liver failure (ACLF) is a clinical syndrome affecting patients with chronic liver disease characterized by abrupt hepatic decompensation and associated with high short-term mortality. It is characterized by intense systemic inflammation, organ failure, and a poor prognosis. Using certain liver-specific prognostic scores, and organ...
Noninvasive diagnosis of lung cancer in early stages is one task where radiomics helps. Clinical practice shows that the size of a nodule has high predictive power for malignancy. In the literature, convolutional neural networks (CNNs) have become widely used in medical image analysis. We study the ability of a CNN to capture nodule size in compute...
Image acquisition parameters for computed tomography scans such as slice thickness and field of view may vary depending on tumor size and site. Recent studies have shown that some radiomics features were dependent on voxel size (= pixel size × slice thickness), and with proper normalization, this voxel size dependency could be reduced. Deep feature...
Convolutional Neural Networks (CNNs) have been utilized for to distinguish between benign lung nodules and those that will become malignant. The objective of this study was to use an ensemble of CNNs to predict which baseline nodules would be diagnosed as lung cancer in a second follow up screening after more than one year. Low-dose helical compute...
p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We...
p>esting for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30\ and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We...
p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We...
Purpose: Due to the high incidence and mortality rates of lung cancer worldwide, early detection of a precancerous lesion is essential. Low-dose computed tomography is a commonly used technique for screening, diagnosis, and prognosis of non-small-cell lung cancer. Recently, convolutional neural networks (CNN) had shown great potential in lung nodul...
p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We...
p>Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We...
Neonatal pain assessment in clinical environments is challenging as it is discontinuous and biased. Facial/body occlusion can occur in such settings due to clinical condition, developmental delays, prone position, or other external factors. In such cases, crying sound can be used to effectively assess neonatal pain. In this paper, we investigate th...
The current standard for assessing neonatal pain is discontinuous and inconsistent because it depends highly on the observers bias. These drawbacks can result in delayed intervention and inconsistent treatment of pain. Convolutional Neural Networks (CNNs) have gained much popularity in the last decades due to the wide range of its successful applic...
Quantitative features are generated from a tumor phenotype by various data characterization, feature-extraction approaches and have been used successfully as a biomarker. These features give us information about a nodule, for example, nodule size, pixel intensity, histogram-based information, and texture information from wavelets or a convolution k...
Semantic features are common radiological traits used to characterize a lesion by a trained radiologist. These features have been recently formulated, quantified on a point scale in the context of lung nodules by our group. Certain radiological semantic traits have been shown to extremely predictive of malignancy [26]. Semantic traits observed by a...
Lung cancer is the leading cause of cancer-related deaths globally, which makes early detection and diagnosis a high priority. Computed tomography (CT) is the method of choice for early detection and diagnosis of lung cancer. Radiomics features extracted from CT-detected lung nodules provide a good platform for early detection, diagnosis, and progn...
In robotics, knowing the object states and recognizing the desired states are very important. Objects at different states would require different grasping. To achieve different states, different manipulations would be required, as well as different grasping. To analyze the objects at different states, a dataset of cooking objects was created. Cooki...
Human does their daily activity and cooking by teaching and imitating with the help of their vision and understanding of the difference between materials. Teaching a robot to do coking and daily work is difficult because of variation in environment, handling objects at different states etc. Pouring is a simple human daily life activity. In this pap...
Lung cancer has a high incidence and mortality rate. Early detection and diagnosis of lung cancers is best achieved with low-dose computed tomography (CT). Classical radiomics features extracted from lung CT images have been shown as able to predict cancer incidence and prognosis. With the advancement of deep learning and convolutional neural netwo...
Osteoporosis can be identified by looking at 2D x-ray images of the bone. The high degree of similarity between images of a healthy bone and a diseased one makes classification a challenge. A good bone texture characterization technique is essential for identifying osteoporosis cases. Standard texture feature extraction techniques like Local Binary...
Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully a...
Questions
Questions (5)
Hi,
I have a question regarding DTI image, which may be very basic.
I generated DTI image, FA map from diffusion weighted images using FSL. I also have ROI files (brainstem region/ hippocampus region) for the same data. Now I want to generate some quantitative values from a ROI.
How can i get the information (number of fibers, length of fibers, and other relevant information) from a particular ROI using FSL or MATLAB or any other tools which supports nifit files?
I am new in this area of research. Any help will be really appreciated.
Thank you.
There are some statistical test to compare the significance test between the two Pearson correlation coefficients (by fisher's r to z transformation.) I want to know if there is any statistical test to compare the significance of the difference between two Concordance correlation correlations. So that it can be compared to each other so one can claim that one is "stronger" than another.
X and Y are not correlated (0.3); however, when I place X in random forests classifier predicting Y, alongside two (A, B) other (related) variables, X and two other variables (A, B) are significant predictors of Y. Note that the two other (A, B) variables are also not correlated with Y.
How can I interpret this according to statistics and machine learning idea?
Representing one or more variable (A, or B or Y) with respect to another variable (X), where the variables don't have a strong correlation.
Suppose I have a dicom file with pixel size (0.5,0.5), and I want to make the pixel size to (0.7,0.7) or vice versa. How can I change/modify the pixel size of a dicom file?
I want to classify images using recurrent neural network, any help on matlab for that.
