no code implementations • 24 Feb 2022 • Sharib Ali, Noha Ghatwary, Debesh Jha, Ece Isik-Polat, Gorkem Polat, Chen Yang, Wuyang Li, Adrian Galdran, Miguel-Ángel González Ballester, Vajira Thambawita, Steven Hicks, Sahadev Poudel, Sang-Woong Lee, Ziyi Jin, Tianyuan Gan, Chenghui Yu, Jiangpeng Yan, Doyeob Yeo, Hyunseok Lee, Nikhil Kumar Tomar, Mahmood Haithmi, Amr Ahmed, Michael A. Riegler, Christian Daul, Pål Halvorsen, Jens Rittscher, Osama E. Salem, Dominique Lamarque, Renato Cannizzaro, Stefano Realdon, Thomas de Lange, James E. East
Polyps are well-known cancer precursors identified by colonoscopy.
no code implementations • 1 Feb 2022 • Natalia Garcia Martin, Stefano Malacrino, Marta Wojciechowska, Leticia Campo, Helen Jones, David C. Wedge, Chris Holmes, Korsuk Sirinukunwattana, Heba Sailem, Clare Verrill, Jens Rittscher
Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions.
While, today most segmentation approaches are supervised and only concentrated on a single modality dataset, this work exploits to use a target-independent unsupervised domain adaptation (UDA) technique that is capable to generalize to an unseen target modality.
2 code implementations • 8 Jun 2021 • Sharib Ali, Debesh Jha, Noha Ghatwary, Stefano Realdon, Renato Cannizzaro, Osama E. Salem, Dominique Lamarque, Christian Daul, Michael A. Riegler, Kim V. Anonsen, Andreas Petlund, Pål Halvorsen, Jens Rittscher, Thomas de Lange, James E. East
To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset curated by a team of computational scientists and expert gastroenterologists.
We propose an end-to-end CNN-based framework for the segmentation of stones and laser fiber.
We propose a novel architecture called feedback attention network (FANet) that unifies the previous epoch mask with the feature map of the current training epoch.
Ranked #1 on Medical Image Segmentation on EM
Our dataset consists of a total of 871 images consisting of both source and target domains.
Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks.
Ranked #10 on Medical Image Segmentation on CVC-ClinicDB
no code implementations • 12 Oct 2020 • Sharib Ali, Mariia Dmitrieva, Noha Ghatwary, Sophia Bano, Gorkem Polat, Alptekin Temizel, Adrian Krenzer, Amar Hekalo, Yun Bo Guo, Bogdan Matuszewski, Mourad Gridach, Irina Voiculescu, Vishnusai Yoganand, Arnav Chavan, Aryan Raj, Nhan T. Nguyen, Dat Q. Tran, Le Duy Huynh, Nicolas Boutry, Shahadate Rezvy, Haijian Chen, Yoon Ho Choi, Anand Subramanian, Velmurugan Balasubramanian, Xiaohong W. Gao, Hongyu Hu, Yusheng Liao, Danail Stoyanov, Christian Daul, Stefano Realdon, Renato Cannizzaro, Dominique Lamarque, Terry Tran-Nguyen, Adam Bailey, Barbara Braden, James East, Jens Rittscher
The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies.
Fine-grained classification of microscopic image data with limited samples is an open problem in computer vision and biomedical imaging.
In this work, we propose to use a few-shot learning approach that requires less training data and can be used to predict label classes of test samples from an unseen dataset.
What could be more important than disease detection and localization?
To address this problem, we propose a novel approach of learning a continuous warp of the source image.
In this study, we propose to use an autoencoder for efficient video compression and fast retrieval of video images.
Removal of marker ink from these high-resolution whole slide images is non-trivial and complex problem as they contaminate different regions and in an inconsistent manner.
no code implementations • 8 May 2019 • Sharib Ali, Felix Zhou, Christian Daul, Barbara Braden, Adam Bailey, Stefano Realdon, James East, Georges Wagnières, Victor Loschenov, Enrico Grisan, Walter Blondel, Jens Rittscher
Endoscopic artifacts are a core challenge in facilitating the diagnosis and treatment of diseases in hollow organs.
Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem.
While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology.