Active surveillance (AS) is the preferred strategy for favorable-risk prostate cancer, yet current protocols rely on scheduled repeat biopsies, most of which reveal no progression and are unnecessary. Existing risk-stratification tools operate on single time-point imaging or depend on explicit lesion segmentation, limiting their ability to capture longitudinal change and excluding patients without an MRI-visible lesion.
The paper introduces ANT, a test‑time adaptation framework that improves prostate cancer detection in micro‑ultrasound by performing a segmentation‑guided adaptation. ANT aligns a pretrained detection encoder to the target domain’s prostate anatomy using pseudo‑masks from a frozen segmentation network, thereby correcting domain‑specific feature drift while preserving cancer‑discriminative features. In a leave‑one‑center‑out evaluation, ANT raises mean AUC by 2.9% at the biopsy‑core level and 3.6% at the patient level compared to no adaptation, outperforming existing TTA baselines.
By Obed Korshie Dzikunu, Mohammad Mahdi Abootorabi, Mohamed Harmanani, Paul F. R. Wilson, Emma Willis, Ferdinand Luger, Adam Kinnaird, Brian Wodlinger, Parvin Mousavi, Purang Abolmaesumi
arXiv:2605. 23995v4 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) is increasingly used in medical image analysis to reduce dependence on costly expert annotations by learning transferable representations from unlabeled data.
By Chathura Wimalasiri, Kishor Nandakishor, Marimuthu Palaniswami
arXiv:2510.14383v4 Announce Type: replace
Abstract: Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-bas...
By Danish Ali, Ajmal Mian, Naveed Akhtar, Ghulam Mubashar Hassan
arXiv:2608. 19666v1 Announce Type: new Abstract: Deep learning-based whole-body PET-CT lesion segmentation can support cancer staging, treatment planning, and response assessment, but generalization is limited by scarce annotations and domain shifts.
By Bashirul Azam Biswas, Amartya Bhattacharya, Biratal Raj Wagle, Matthew E. Maeder, James B. Yu, Indrani Bhattacharya
The paper introduces MSM‑Seg, a dual‑memory segmentation framework for 3D multi‑modal brain tumor segmentation. It combines a modality‑and‑slice memory attention module to capture cross‑modal and spatial‑slice dependencies, a multi‑scale category‑agnostic prompt encoder for whole‑tumor guidance, and a modality‑adaptive fusion decoder to integrate complementary decoding information. Experiments on various MRI datasets show that MSM‑Seg surpasses state‑of‑the‑art methods for metastases and glioma tumor segmentation.
By Yuxiang Luo, Qing Xu, Hai Huang, Yuqi Ouyang, Xiangjian He, Zhen Chen, Wenting Duan, Jiebo Luo
arXiv:2607. 17782v1 Announce Type: cross Abstract: Foundation models pretrained using self-supervised learning have transformed computer vision by learning transferable representations from large-scale unlabeled data.
By Moona Mazher, Abdul Qayyum, Steven A. Niederer, Daniel C. Alexander
arXiv:2608.23745v1 Announce Type: cross
Abstract: Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and diseas...
By Mohammad Mahdi Danesh Pajouh, Sara Saeedi
arXiv:2606. 30951v1 Announce Type: cross Abstract: Micro-ultrasound ($\mu$US) is a new, emerging, and promising imaging modality for prostate cancer (PCa) detection, but accurate identification of suspicious tissue remains highly dependent on clinical experience, leading to substantial inter-observer variability.
By Mohammad Mahdi Abootorabi, Sina Namazi, Armin Saadat, Lyuyang Wang, Obed Dzikunu, Paul F. R. Wilson, Zhuoxin Guo, Brian Wodlinger, Parvin Mousavi, Purang Abolmaesumi
arXiv:2512. 14937v2 Announce Type: replace-cross Abstract: Gliomas are the most common malignant brain tumors in adults and are among the most lethal.
By Abhijeet Parida, Daniel Capell\'an-Mart\'in, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, Mar\'ia J. Ledesma-Carbayo, Marius George Linguraru
arXiv:2607. 06919v1 Announce Type: cross Abstract: Artificial intelligence (AI) analysis of micro-ultrasound ($\mu$US) has shown promise for prostate cancer (PCa) detection.
By Paul F. R. Wilson, Mohamed Harmanani, Zhuoxin Guo, Obed K. Dzikunu, Hannes Cash, Adam Kinnaird, Brian Wodlinger, Purang Abolmaesumi, Parvin Mousavi
arXiv:2605.05522v3 Announce Type: replace-cross
Abstract: Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities s...
By Aneesh Rangnekar, Joao Miranda, Natally Horvat, Stephanie Chahwan, Samir Alrayess, Aditya Apte, Aditi Iyer, Eve LoCastro, Revathi Ravella, Marc J Gollub, Iva Petkovska, Jesse Joshua Smith, Paul Romesser, Julio Garcia-Aguilar, Harini Veeraraghavan, Joseph O Deasy