Semi-supervised Pathology Segmentation with Disentangled Representations

Jiang, Haochuan; Chartsias, Agisilaos; Zhang, Xinheng; Papanastasiou, Giorgos; Semple, Scott; Dweck, Marc R; Semple, David; Dharmakumar, Rohan; Tsaftaris, Sotirios A.

doi:10.1007/978-3-030-60548-3_7

Cited by 3 publications

(1 citation statement)

References 20 publications

(54 reference statements)

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“…Image segmentation is an important aspect in the field MIA, as it is a necessary intermediate step towards extracting a region of interest within the organ under investigation [142][143][144][145][146]. Although UNet models revolutionised medical image segmentation [147,148], image segmentation remains an open challenge as it relies on strong supervision, hence, a large fraction of labelled data are required.…”

Section: Segmentationmentioning

confidence: 99%

Is Attention all You Need in Medical Image Analysis? A Review

Papanastasiou,

Dikaios,

Huang

et al. 2024

IEEE J. Biomed. Health Inform.

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Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can efficiently model local pixel interactions and be trained on small-scale MI data. Despite their important advances, typical CNN have relatively limited capabilities in modelling "global" pixel interactions, which restricts their generalisation ability to understand out-ofdistribution data with different "global" information. The recent progress of Artificial Intelligence gave rise to Transformers, which can learn global relationships from data. However, full Transformer models need to be trained on large-scale data and involve tremendous computational complexity. Attention and Transformer compartments ("Transf/Attention") which can well maintain properties for modelling global relationships, have been proposed as lighter alternatives of full Transformers. Recently, there is an increasing trend to co-pollinate complementary local-global properties from CNN and Transf/Attention architectures, which led to a new era of hybrid models. The past years have witnessed substantial growth in hybrid CNN-Transf/Attention models across diverse MIA problems. In this systematic review, we survey existing hybrid CNN-Transf/Attention models, review and unravel key architectural designs, analyse breakthroughs, and evaluate current and future opportunities as well as challenges. We also introduced an analysis framework on generalisation opportunities of scientific and clinical impact, based on which new data-driven domain generalisation and adaptation methods can be stimulated.

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Section: Segmentationmentioning

confidence: 99%

Is Attention all You Need in Medical Image Analysis? A Review

Papanastasiou,

Dikaios,

Huang

et al. 2024

IEEE J. Biomed. Health Inform.

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Interpretability-Guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data

Corbetta,

Beets-Tan,

Silva

2023

Lecture Notes in Computer Science

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Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

Ankolekar,

Boie,

Abdollahyan

et al. 2024

Preprint

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Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care.

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Semi-supervised Pathology Segmentation with Disentangled Representations

Cited by 3 publications

References 20 publications

Is Attention all You Need in Medical Image Analysis? A Review

Is Attention all You Need in Medical Image Analysis? A Review

Interpretability-Guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data

Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review

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