405 citations · 993 across the 35 of their papers we have counts for
37 papers
A multi-center analysis of deep learning methods for video polyp detection and segmentation
Noha Ghatwary, Pedro Chavarias Solano, Mohamed Ramzy Ibrahim +24
Colonic polyps are well-recognized precursors to colorectal cancer (CRC), typically detected during colonoscopy. However, the variability in appearance, location, and size of these…
Validating polyp and instrument segmentation methods in colonoscopy through Medico 2020 and MedAI 2021 Challenges
Debesh Jha, Vanshali Sharma, Debapriya Banik +30
Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps durin…
A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation
Ronast Subedi, Rebati Raman Gaire, Sharib Ali +3
This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belo…
SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis
Ziang Xu, Jens Rittscher, Sharib Ali
Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popular. However, they require a la…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
SUPRA: Superpixel Guided Loss for Improved Multi-modal Segmentation in Endoscopy
Rafael Martinez-Garcia-Peña, Mansoor Ali Teevno, Gilberto Ochoa-Ruiz +1
Domain shift is a well-known problem in the medical imaging community. In particular, for endoscopic image analysis where the data can have different modalities the performance of…
A comprehensive survey on recent deep learning-based methods applied to surgical data
Mansoor Ali, Rafael Martinez Garcia Pena, Gilberto Ochoa Ruiz +1
Minimally invasive surgery is highly operator dependant with a lengthy procedural time causing fatigue to surgeon and risks to patients such as injury to organs, infection, bleedin…
A semi-supervised Teacher-Student framework for surgical tool detection and localization
Mansoor Ali, Gilberto Ochoa-Ruiz, Sharib Ali
Surgical tool detection in minimally invasive surgery is an essential part of computer-assisted interventions. Current approaches are mostly based on supervised methods which requi…
Knowledge distillation with a class-aware loss for endoscopic disease detection
Pedro E. Chavarrias-Solanon, Mansoor Ali-Teevno, Gilberto Ochoa-Ruiz +1
Prevalence of gastrointestinal (GI) cancer is growing alarmingly every year leading to a substantial increase in the mortality rate. Endoscopic detection is providing crucial diagn…
Patch-level instance-group discrimination with pretext-invariant learning for colitis scoring
Ziang Xu, Sharib Ali, Soumya Gupta +3
Inflammatory bowel disease (IBD), in particular ulcerative colitis (UC), is graded by endoscopists and this assessment is the basis for risk stratification and therapy monitoring.…
TGANet: Text-guided attention for improved polyp segmentation
Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci +1
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…
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Sharib Ali, Noha Ghatwary, Debesh Jha +29
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and charac…
GMSRF-Net: An improved generalizability with global multi-scale residual fusion network for polyp segmentation
Abhishek Srivastava, Sukalpa Chanda, Debesh Jha +2
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…
Real-time Instance Segmentation of Surgical Instruments using Attention and Multi-scale Feature Fusion
Juan Carlos Angeles-Ceron, Gilberto Ochoa-Ruiz, Leonardo Chang +1
Precise instrument segmentation aid surgeons to navigate the body more easily and increase patient safety. While accurate tracking of surgical instruments in real-time plays a cruc…
Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
Debesh Jha, Sharib Ali, Nikhil Kumar Tomar +4
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…
EndoUDA: A modality independent segmentation approach for endoscopy imaging
Numan Celik, Sharib Ali, Soumya Gupta +2
Gastrointestinal (GI) cancer precursors require frequent monitoring for risk stratification of patients. Automated segmentation methods can help to assess risk areas more accuratel…
A multi-centre polyp detection and segmentation dataset for generalisability assessment
Sharib Ali, Debesh Jha, Noha Ghatwary +12
Polyps in the colon are widely known cancer precursors identified by colonoscopy. Whilst most polyps are benign, the polyp's number, size and surface structure are linked to the ri…
Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
Rabindra Khadga, Debesh Jha, Steven Hicks +4
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 appli…
MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
Abhishek Srivastava, Debesh Jha, Sukalpa Chanda +6
Methods based on convolutional neural networks have improved the performance of biomedical image segmentation. However, most of these methods cannot efficiently segment objects of…
Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures
Juan Carlos Angeles Ceron, Leonardo Chang, Gilberto Ochoa-Ruiz +1
Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer visi…
NanoNet: Real-Time Polyp Segmentation in Video Capsule Endoscopy and Colonoscopy
Debesh Jha, Nikhil Kumar Tomar, Sharib Ali +5
Deep learning in gastrointestinal endoscopy can assist to improve clinical performance and be helpful to assess lesions more accurately. To this extent, semantic segmentation metho…
Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy
Soumya Gupta, Sharib Ali, Louise Goldsmith +2
Kidney stones represent a considerable burden for public health-care systems. Ureteroscopy with laser lithotripsy has evolved as the most commonly used technique for the treatment…
FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
Nikhil Kumar Tomar, Debesh Jha, Michael A. Riegler +5
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. Imag…