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AI in Medicine and Surgery (AIMS group)

AI in Medicine and Surgery (AIMS group)

Dr Sharib Ali

Lecturer/Researcher in Artificial Intelligence/Computer vision
PhD and Master's by research in Computer Vision

Pedro Chavarrias Solano

PhD research student
(video 3D reconstruction)

Gerardo Loza Galindo

PhD research student
(detection and tracking - automation in surgery)

Raneem Toman

PhD research student
(multi-omics data analysis)

Edward Ellis

PhD research student
(ultrasound image analysis)

Patryk Wasniewski

PhD research student
(biomedical high-throughput data analysis)

Mansoor Ali Teevno

Visiting PhD research student
(surgical computer vision)

Current Research/PhD co-supervision

Ziang Xu - University of Oxford, UK
Xukun Zhang - Fudan University, China
Aya Hammad - University of York, York, UK
Maksim Richards - University of Oxford, Oxford, UK
Ajay Patha - NAAMII, Kathmandu, Nepal
Helena Valencia - Tec de Monterrey, Mexico

Past students

Darshita Budhadev, MRes graduate, University of Leeds (Now AI Researcher at Hartree Centre, STFC Daresbury, UK)
Soumya Gupta, DPhil graduate from the University of Oxford (Now Research Engineer at Vision RT Ltd, London, UK)
Juan Carlos Ángeles Cerón, Master's graduate from Tec de Monterrey (Now Data and Applied Scientist II at Microsoft, Washington, US)
Shruti Shrestha, research intern (Now Machine Learning Research Scientist @ Georgia State University, Atlanta, US)
Nikhil K Tomar, research intern (Now working with collaborators at the Northwestern University, Chicago, US)

Open positions in the group

Only self-funded opportunities are available in biomedical and medical image analysis. Please get in touch with me to discuss potential projects.

Sample projects:

Project #1: Federated learning for tackling multimodal and class-imbalance problems in healthcare

Project #2: Artificial Intelligence for Wearables data analysis for personalised cancer treatment

News!!!

  • Crohn's and Colitis funding received of value £98K
  • Dr Sharib Ali serves as the program committee chair for the 37th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS2024), to be held on 26-28 June 2024 in Guadalajara, Mexico.
  • Invited keynote (Dr Sharib Ali) at the 22nd MICAI International conference.
  • Our paper received the Outstanding Paper award at the AE-CAI workshop at MICCAI 2023. Congratulations to Gerardo Loza (the first author) and the team.
  • Six accepted papers at MICCAI2023 - Congratulations to all members of the group!!!
    • 1 paper was accepted at the MICCAI main conference
    • 5 papers at MICCAI workshops (1 paper at CaPTion, 2 at DEMI workshop, 1 paper at AE-CAI workshop and 1 paper at FAIMI)
  • We are organising two workshops at the MICCAI 2023, Vancouver, Canada
  • Darshita successfully defended her MRes viva - Congratulations to her!
  • Invited Podcast interview on "AI-powered Endoscopic Image Analysis" - 27th March 2023
  • Invited talk of Dr Sharib Ali at the UCB (Union Chimique Belge) - 14th June 2023
  • Guest Lecture by Dr Sharib Ali at the University of Aberdeen's research group - 31st July 2023
  • Darshita has joined the group as part of her Master's by a research project on the segmentation of membranes in cryo-tomography data (May'23-Sep'23)
  • Mansoor Ali is visiting us AIMS group. He delivered a talk to the group on 7th August'23 at our weekly technical meeting (Aug'23-Dec'23)

Research highlights

Robust and smooth Couinaud segmentation via point-voxel network (New)

We introduce a novel multi-scale point-voxel fusion framework for fully automated Couinaud segmentation, a critical task for liver surgery planning. By innovatively leveraging the topological relationships of coordinate points in 3D space and the rich semantic information encoded in voxel grids, our method not only recognizes but also intricately understands the spatial hierarchies and relationships crucial for precise Couinaud segmentation. Moreover, our approach is the integration of the dense point sampling strategy, enriched with vessel priors, which significantly enhances our model’s focus on critical areas. This strategy facilitates a detailed understanding of the trajectories of key vascular structures, thus paving the way for safer surgical pathways that substantially minimize the risk of damaging major blood vessels.

Published at Computers in Biology and Medicine (IF: 7.0)

Self-supervised learning with composite pretext-class discrimination (New)

Despite the publicly available datasets and datasets that can be generated within hospitals, most supervised models still underperform. While self-supervised learning has addressed this problem to some extent in natural scene data, there is a considerable performance gap in the medical image domain. In this paper, we propose to explore patch-level instance-group discrimination and penalisation of inter- class variation using additive angular margin within the co- sine similarity metrics. Our novel approach enables models to learn to cluster similar representations, thereby improv- ing their ability to provide better separation between differ- ent classes. Our results demonstrate significant improvement on all metrics over the state-of-the-art (SOTA) methods on the test set from the same and diverse datasets.

Published at: IEEE Transactions in Medical Imaging (IF: 8.9)

Assessing generalisability of deep learning-based in colonoscopy

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.

Published at: Nature Scientific Reports

Where do we stand in AI for endoscopic image analysis?

The paper reviews recent works on endoscopic image analysis with artificial intelligence (AI) and emphasises the current unmatched needs in this field. Finally, it outlines the future directions for clinically relevant complex AI solutions to improve patient outcomes. Published at npj Digit. Med. (December 2022) - Click to read more.

Real-time surgical tool detection with multi-scale positional encoding & CL

We propose an anchor-free architecture based on a transformer that utilises multi-scale features within the feature extraction layer and at the transformer-based detection architecture through positional encoding that can refine and capture context-aware and structural information of different-sized tools. Furthermore, a supervised contrastive loss is introduced to optimize representations of object embeddings, resulting in improved feed-forward network performances for classifying localized bounding boxes. Compared to the most accurate existing SOTA method, our approach has an improvement of nearly 4% on mAP50 and a reduction in the inference time by 113%. It also showed a 7% higher mAP50 than the baseline model.

Published at: Wiley, Health Technology Letters

Anatomical-aware Point-Voxel Network for Couinaud Segmentation in Liver CT

We design a multi-scale point-voxel fusion network to capture the anatomical structure and semantic information of the liver and vessels, respectively, while also increasing important data access through vessel structure prior. Finally, the network outputs the classification of Couinaud segments in the continuous liver space, producing a more accurate and smooth 3D Couinaud segmentation mask. Our proposed method outperforms several state-of-the-art methods, both point-based and voxel-based, as demonstrated by our experimental results on two public liver datasets.

Published at: MICCAI conference 2023

A semi-supervised Teacher-Student framework for surgical tool localisation

Semi-supervised learning (SSL) has recently emerged as a viable alternative showing promise in producing models retaining competitive performance to supervised methods. This paper introduces an SSL framework in the surgical tool detection paradigm, which aims to mitigate training data scarcity and data imbalance problems through a knowledge distillation approach.


Published at: MICCAI conference workshop AE-CAI'2022

Selected/latest publications

Conference (selected)

  • Xukun Zhang, Yang Liu, Sharib Ali, and others. Anatomical-aware Point-Voxel Network for Couinaud Segmentation in Liver CT. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023.
  • Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J Adler, Sharib Ali and others. Why is the winner the best? Proceedings of the  IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023.
  • Rafael Martínez-García-Peña, Mansoor Ali Teevno, Gilberto Ochoa-Ruiz, and Sharib Ali.
    SUPRA: Superpixel Guided Loss for Improved Multi-Modal Segmentation in Endoscopy. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 285-294
  • Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci, and Sharib Ali. TGANet: Text-guided attention for improved polyp segmentation, Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022, pp. 151–160.
  • Abhishek Srivastava, Sukulpa Chanda, Debesh Jha, Umapada Pal and Sharib Ali, "GMSRF-Net: An Improved generalizability with Global Multi-Scale Residual Fusion Network for Polyp Segmentation," 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 4321-4327.
  • Pedro E. Chavarrias-Solano, Mansoor A. Teevno, Gilberto Ochoa-Ruiz and Sharib Ali. Knowledge Distillation with a Class-Aware Loss for Endoscopic Disease Detection. Cancer Prevention Through Early Detection. CaPTion 2022. Lecture Notes in Computer Science, vol 13581, pp. 67–76
  • Soumya Gupta, Sharib Ali, Ziang Xu, Binod Bhattarai, Ben Turney and Jens Rittscher. UNet-eVAE: Iterative Refinement Using VAE Embodied Learning for Endoscopic Image Segmentation. Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583, pp. 161–170.
  • Ziang Xu, Sharib Ali, Soumya Gupta et al. (2022). Patch-Level Instance-Group Discrimination with Pretext-Invariant Learning for Colitis Scoring. In: Machine Learning in Medical Imaging. MLMI 2022. Lecture Notes in Computer Science, vol 13583. Springer, Cham. https://doi.org/10.1007/978-3-031-21014-3_11

Journals (selected)

  • Ziang Xu, Jens Rittscher and Sharib Ali, "SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2024.3411933 (new article), June 2024.
  • Sharib Ali, Noha Ghatwary, Debesh Jha et al. Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge. Sci Rep 14, 2032 (2024). https://doi.org/10.1038/s41598-024-52063-x (New article)
  • Gerardo Loza,  Pietro Valdastri,  Sharib Ali. Real-time surgical tool detection with multi-scale positional encoding and contrastive learning. Healthc. Technol. Lett. 00, 1–11 (2023). https://doi.org/10.1049/htl2.12060
  • Sharib Ali, Debesh Jha, Noha Ghatwary, Stefano Realdon and others. A multi-centre polyp detection and segmentation dataset for generalisability assessment. Scientific Data. 2023; 10(1):75. https://doi.org/10.1038/s41597-023-01981-y
  • Sharib Ali. Where do we stand in AI for endoscopic image analysis? Deciphering gaps and future directions. npj Digital Medicine. 2022; 5:184. https://doi.org/10.1038/s41746-022-00733-3
  • Mansoor Ali, Gilberto Ochoa-Ruiz and Sharib Ali. A semi-supervised Teacher-Student framework for surgical tool detection and localization. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2022. https://doi.org/10.1080/21681163.2022.2150688
  • Juan Carlos Angeles Ceron, Gilberto Ochoa Ruiz, Leonardo Chang and Sharib Ali. Real-time instance segmentation of surgical instruments using attention and multi-scale feature fusion. Medical Image Analysis. 2022; 81:102569. https://doi.org/10.1016/j.media.2022.102569
  • Soumya Gupta, Sharib Ali, Louise Goldsmith, Ben Turney and Jens Rittscher. Multi-class motion-based semantic segmentation for ureteroscopy and laser lithotripsy. Computerized Medical Imaging and Graphics. 2022;101:102112.

Grants & awards

Optimise project

Novel robust computer vision methods and synthetic datasets for minimally invasive surgery
£10,000 for 12 Months (co-I)

Bowel cancer prediction

£98,691 for 24 Months (co-I)

Collaborators

We collaborate with several clinical colleagues at the Leeds Teaching Hospital Trust  and other hospitals in the UK including Oxford University Hospitals, Oxford. We have also extended collaboration across Europe (France, Italy, Sweden, and Germany), Canada, Asia (China, Nepal)  and Africa (Egypt). Thanks to all colleagues for their continuous support in our research that we do. If your research resonate to something we do please reach out to us at s.s.ali[at]leeds[dot]ac[dot]uk