| 1. |
- Ali, Sharib, et al.
(författare)
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A multi-centre polyp detection and segmentation dataset for generalisability assessment.
- 2023
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Ingår i: Scientific data. - : Springer Science and Business Media LLC. - 2052-4463. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- 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 risk of colon cancer. Several methods have been developed to automate polyp detection and segmentation. However, the main issue is that they are not tested rigorously on a large multicentre purpose-built dataset, one reason being the lack of a comprehensive public dataset. As a result, the developed methods may not generalise to different population datasets. To this extent, we have curated a dataset from six unique centres incorporating more than 300 patients. The dataset includes both single frame and sequence data with 3762 annotated polyp labels with precise delineation of polyp boundaries verified by six senior gastroenterologists. To our knowledge, this is the most comprehensive detection and pixel-level segmentation dataset (referred to as PolypGen) curated by a team of computational scientists and expert gastroenterologists. The paper provides insight into data construction and annotation strategies, quality assurance, and technical validation.
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| 2. |
- Ali, Sharib, et al.
(författare)
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Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
- 2024
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Ingår i: SCIENTIFIC REPORTS. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.
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| 3. |
- de Groot, Frank M. F., et al.
(författare)
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2p x-ray absorption spectroscopy of 3d transition metal systems
- 2021
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Ingår i: Journal of Electron Spectroscopy and Related Phenomena. - : Elsevier BV. - 0368-2048 .- 1873-2526. ; 249
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Tidskriftsartikel (refereegranskat)abstract
- This review provides an overview of the different methods and computer codes that are used to interpret 2p x-ray absorption spectra of 3d transition metal ions. We first introduce the basic parameters and give an overview of the methods used. We start with the semi-empirical multiplet codes and compare the different codes that are available. A special chapter is devoted to the user friendly interfaces that have been written on the basis of these codes. Next we discuss the first principle codes based on band structure, including a chapter on Density Functional theory based approaches. We also give an overview of the first-principle multiplet codes that start from a cluster calculation and we discuss the wavefunction based methods, including multi-reference methods. We end the review with a discussion of the link between theory and experiment and discuss the open issues in the spectral analysis.
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