Debesh Jha

Debesh Jha
Northwestern University | NU · Department of Radiology

PhD in computer science
Medical image analysis, deep learning, Computer vision, and Colonoscopy dataset development.

About

97
Publications
24,135
Reads
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1,808
Citations
Citations since 2016
97 Research Items
1797 Citations
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Introduction
Our mission is to improve healthcare in a data-driven manner. To this end, we work on diverse medical problems and build novel deep-learning solutions for diagnosis, prognosis and treatment and release medical datasets for reproducible & transparent machine learning.
Additional affiliations
February 2018 - August 2019
Simula Research Laboratory
Position
  • PhD Student
February 2018 - present
UiT The Arctic University of Norway
Position
  • PhD Student
September 2015 - January 2018
Chosun University
Position
  • Research Assistant
Education
February 2018 - January 2021
UiT The Arctic University of Norway
Field of study
  • Computer Science
September 2015 - August 2017
Chosun University
Field of study
  • Information and Communication Engineering
September 2008 - September 2013

Publications

Publications (97)
Preprint
Full-text available
Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscop...
Preprint
Full-text available
Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called Doub...
Article
Full-text available
Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify re...
Article
Full-text available
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer-vision method...
Conference Paper
Full-text available
Pixel-wise image segmentation is a highly demanding task in medical-image analysis. In practice, it is difficult to find annotated medical images with corresponding segmentation masks. In this paper, we present Kvasir-SEG: an open-access dataset of gastrointestinal polyp images and corresponding segmentation masks, manually annotated by a medical d...
Preprint
Full-text available
Head and Neck (H\&N) organ-at-risk (OAR) and tumor segmentations are essential components of radiation therapy planning. The varying anatomic locations and dimensions of H\&N nodal Gross Tumor Volumes (GTVn) and H\&N primary gross tumor volume (GTVp) are difficult to obtain due to lack of accurate and reliable delineation methods. The downstream ef...
Preprint
Full-text available
Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Excision of polyps during colonoscopy helps reduce mortality and morbidity for CRC. Powered by deep learning, computer-aided diagnosis (CAD) systems can detect regions in the colon overlooked by physicians during colonoscopy. Lacking high accuracy and real-time s...
Chapter
Full-text available
Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limitin...
Preprint
Full-text available
Accurate segmentation of organs-at-risks (OARs) is a precursor for optimizing radiation therapy planning. Existing deep learning-based multi-scale fusion architectures have demonstrated a tremendous capacity for 2D medical image segmentation. The key to their success is aggregating global context and maintaining high resolution representations. How...
Preprint
Full-text available
With the advent of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML)/DeepLearning (DL) algorithms, the data-driven medical application has emerged as a promising tool for designing reliable and scalable diagnostic and prognostic models from medical data. Hence, in recent years, data-driven medical applications ha...
Chapter
Full-text available
Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolution neural networks (CNNs) have became the standard image segmentation tasks, more recently this has started to change towards Transformers based...
Preprint
Full-text available
Due to the current COVID-19 virus, which has already been declared a pandemic by the World Health Organization (WHO), we are witnessing the greatest pandemic of the decade. Millions of people are being infected, resulting in thousands of deaths every day across the globe. Even it was difficult for the best healthcare-providing countries could not h...
Preprint
Full-text available
Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to...
Preprint
Full-text available
Colorectal cancer (CRC) is one of the most common causes of cancer and cancer-related mortality worldwide. Performing colon cancer screening in a timely fashion is the key to early detection. Colonoscopy is the primary modality used to diagnose colon cancer. However, the miss rate of polyps, adenomas and advanced adenomas remains significantly high...
Preprint
Full-text available
Video capsule endoscopy is a hot topic in computer vision and medicine. Deep learning can have a positive impact on the future of video capsule endoscopy technology. It can improve the anomaly detection rate, reduce physicians' time for screening, and aid in real-world clinical analysis. CADx classification system for video capsule endoscopy has sh...
Preprint
Full-text available
The detection and removal of precancerous polyps through colonoscopy is the primary technique for the prevention of colorectal cancer worldwide. However, the miss rate of colorectal polyp varies significantly among the endoscopists. It is well known that a computer-aided diagnosis (CAD) system can assist endoscopists in detecting colon polyps and m...
Preprint
Full-text available
Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the standard image segmentation tasks, more recently this has started to change towards Transformers base...
Preprint
Full-text available
Colonoscopy is a gold standard procedure but is highly operator-dependent. Automated polyp segmentation, a precancerous precursor, can minimize missed rates and timely treatment of colon cancer at an early stage. Even though there are deep learning methods developed for this task, variability in polyp size can impact model training, thereby limitin...
Article
Full-text available
The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the success of deep learning ap...
Preprint
Full-text available
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and characterisation. Moreover, colonoscopic surveillance and removal of polyps (referred to as polypectomy ) are highly operator-dependent procedures. There exist a high missed de...
Article
Full-text available
Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patien...
Chapter
Full-text available
The holy grail in endoscopy examinations has for a long time been assisted diagnosis using Artificial Intelligence (AI). Recent developments in computer hardware are now enabling technology to equip clinicians with promising tools for computer-assisted diagnosis (CAD) systems. However, creating viable models or architectures, training them, and ass...
Article
Full-text available
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common for biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to ad...
Conference Paper
Full-text available
Colorectal cancer is one of the major cause of cancer-related death around the world. High-quality colonoscopy is considered mandatory for resecting and preventing colorectal cancers. In the recent past, various technological advances have been made towards improving the quality of colonoscopy. Despite the technical advancement , some polyps are fr...
Conference Paper
Full-text available
Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing the location of a disease can play a vital role in treatment and decision-making. Convolutional neural network (CNN) based encoder-decoder techniques have advanced the per...
Preprint
Full-text available
Medical image segmentation can provide detailed information for clinical analysis which can be useful for scenarios where the detailed location of a finding is important. Knowing the location of disease can play a vital role in treatment and decision-making. Convolutional neural network (CNN) based encoder-decoder techniques have advanced the perfo...
Preprint
Full-text available
Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy....
Article
Full-text available
Colorectal cancer is one of the major causes of cancer-related deaths globally. Although colonoscopy is considered as the gold standard for examination of colon polyps, there is a significant miss rate of around 22-28 %. Deep learning algorithms such as convolutional neural networks can aid in the detection and describe abnormalities in the colon t...
Article
Full-text available
MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems. We propose three tasks to meet specific gastrointestinal image segmentation challenges collected from experts within the field, including t...
Preprint
Full-text available
Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These a...
Chapter
Existing video polyp segmentation(VPS) models typically employ convolutional neural networks (CNNs) to extract features. However, due to their limited receptive fields, CNNs cannot fully exploit the global temporal and spatial information in successive video frames, resulting in false positive segmentation results. In this paper, we propose the nov...
Conference Paper
Full-text available
Minimally Invasive Surgery (MIS) is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have open potential for computer-assisted...
Preprint
Full-text available
Colonoscopy is considered the gold standard for detection of colorectal cancer and its precursors. Existing examination methods are, however, hampered by high overall miss-rate, and many abnormalities are left undetected. Computer-Aided Diagnosis systems based on advanced machine learning algorithms are touted as a game-changer that can identify re...
Preprint
Full-text available
Minimally invasive surgery is a surgical intervention used to examine the organs inside the abdomen and has been widely used due to its effectiveness over open surgery. Due to the hardware improvements such as high definition cameras, this procedure has significantly improved and new software methods have demonstrated potential for computer-assiste...
Book
Full-text available
This volume contains the proceedings of the third edition of the inter- national workshop and challenge on computer vision in endoscopy- (EndoCV). Due to the COVID-19 outbreak, the workshop was vir- tually held as a webinar on the 13th Arpil 2021 (initially planned to be held in Nice, France). For the third time this challenge was co- located with...
Preprint
Full-text available
Polyps in the colon are widely known as cancer precursors identified by colonoscopy either related to diagnostic work-up for symptoms, colorectal cancer screening or systematic surveillance of certain diseases. Whilst most polyps are benign, the number, size and the surface structure of the polyp are tightly linked to the risk of colon cancer. Ther...
Preprint
Full-text available
Classical supervised methods commonly used often suffer from the requirement of an abudant number of training samples and are unable to generalize on unseen datasets. As a result, the broader application of any trained model is very limited in clinical settings. However, few-shot approaches can minimize the need for enormous reliable ground truth l...
Article
Full-text available
Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for im...
Preprint
Full-text available
Existing video polyp segmentation (VPS) models typically employ convolutional neural networks (CNNs) to extract features. However, due to their limited receptive fields, CNNs can not fully exploit the global temporal and spatial information in successive video frames, resulting in false-positive segmentation results. In this paper, we propose the n...
Preprint
Full-text available
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of variable sizes and train on small and biased datasets, which are common in biomedical use cases. While methods exist that incorporate multi-scale fusion approaches to add...
Preprint
Full-text available
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation methods that can perform automated real-time delineation of a region-of-interest, e.g., boundary identification of cancer or precancerous lesions, can benefit both diagnosis a...
Preprint
Full-text available
With the increase in available large clinical and experimental datasets, there has been substantial amount of work being done on addressing the challenges in the area of biomedical image analysis. Image segmentation, which is crucial for any quantitative analysis, has especially attracted attention. Recent hardware advancement has led to the succes...
Preprint
Full-text available
Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer vision method...
Chapter
Full-text available
Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud hosted infrastructur...
Chapter
Full-text available
The EndoTect challenge at the International Conference on Pattern Recognition 2020 aims to motivate the development of algorithms that aid medical experts in finding anomalies that commonly occur in the gastrointestinal tract. Using HyperKvasir, a large dataset containing images taken from several endoscopies, the participants competed in three tas...
Chapter
Full-text available
Colonoscopy is the gold standard for examination and detection of colorectal polyps. Localization and delineation of polyps can play a vital role in treatment (e.g., surgical planning) and prognostic decision making. Polyp segmentation can provide detailed boundary information for clinical analysis. Convolutional neural networks have improved the p...