Newest papers

Reinforcing intra-operative tumour localisation with weak supervision from pre-operative appearances

Sidhart Sami, Ali Sharjeel, Sri Atluri, Miao Zhao, Caroline Roney, Eirini Marouli, Shaheer U. Saeed · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31253 · cited by 0 works (OpenAlex) · open-access PDF

Korean

수술 중인 이미지에 대한 유한한 종양 가시성은 수술 중인 변형과 수술 중인 사전 및 수술 중인 이미지의 부족으로 인해 수술 중에 종양 존재를 분류하는 데 어려움을 겪고 있으며, 수술 중에 종양 존재를 분류하는 것은 여전히 어려움을 겪습니다. 우리는 수술 중에 종양 존재를 분류하기 위해 종양 존재를 지시하기 위해 종양 현상 (MR) 이미지로부터 배운 종양 모습을 전송하도록 제안합니다. 분류자는 종양 존재를 수술 이전 이미지에 분류하기 위해 훈련을 받았습니다. 이것은 수술 중인 이미지의 창문에 적용되며, 종양 존재 확률이 종양을 포함하는 창문에 평균적으로 높아지는 도메인 간격에도 불구하고. 이러한 약한

Machine translation (Meta M2M100); the English original follows.

Abstract

Limited tumour visibility on intra-operative images complicates surgical navigation, including liver hepatocellular carcinoma (HCC) targeting on computed tomography (CT) imaging. This makes labelled datasets difficult to curate for deep learning, and transferring pre-operative delineations through registration remains challenging due to intra-operative deformation and lack of paired pre- and intra-operative data. We propose to transfer tumour appearances learned from pre-operative magnetic resonance (MR) images to guide localisation on intra-operative CT through weak supervision. A classifier is trained to classify tumour presence on pre-operative images. This is applied to windows on intra-operative images, and despite the domain gap, it assigns higher tumour-presence probabilities on average to tumour-containing windows. These weak cross-domain signals guide a reinforcement learning agent to localise tumours on intra-operative images. We compare direct transfer, few-shot adaptation and synthetic transfer (trained using unpaired MR and CT), which translates CT into synthetic MR before localisation. On an external HCC cohort, synthetic transfer outperformed other tested methods and CT-only baselines. Our framework enables cross-modality tumour localisation without paired MR-CT training images.

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Topics: Artificial intelligence, Computed tomography, Computer science, Transfer of learning, Magnetic resonance imaging, Computer vision

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