The study addresses the challenge of limited tumour visibility in intra‑operative CT images for liver hepatocellular carcinoma (HCC) surgery. It proposes a weakly supervised approach that transfers tumour appearance knowledge from pre‑operative MR images to guide localisation on intra‑operative CT, using a classifier and reinforcement learning. Experiments on an external HCC cohort show that synthetic transfer—converting CT to synthetic MR before localisation—outperforms other methods and CT‑only baselines, enabling cross‑modality tumour localisation without paired MR‑CT training data.
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
arXiv:2607. 19137v1 Announce Type: cross Abstract: Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy.
By Andrea Borghesi, Xin Wang, Jonas Teuwen, George Yiasemis
The paper introduces Order‑Aware Slab Multiple Instance Learning (OAS‑MIL), a weakly supervised method that predicts perineural invasion (PNI) risk in intrahepatic cholangiocarcinoma using preoperative T2‑weighted MRI. Tumor‑centered MRI crops are split into ordered 2.5D slabs, and a shared encoder extracts features that are aggregated through both permutation‑invariant set‑attention and bidirectional sequence‑attention branches. In five‑fold patient‑level cross‑validation, OAS‑MIL achieved a mean AUROC of 0.770, outperforming volumetric and other MIL baselines, indicating that preserving axial order improves non‑invasive PNI prediction.
By Hyunsu Go, Youngung Han, Kyeonghun Kim, Jinyong Jun, Junbeom Lee, Dohyun Kweon, Yului Jeong, Suah Park, Sungha Park, Anna Jung, Woo Kyoung Jeong, Ken Ying-Kai Liao, Hyuk-Jae Lee, Nam-Joon Kim
The paper introduces Order-Aware Slab Multiple Instance Learning (OAS‑MIL) for predicting perineural invasion (PNI) in intrahepatic cholangiocarcinoma using preoperative T2‑weighted MRI. It treats each tumor‑centered MRI crop as an ordered sequence of overlapping 2.5D slabs, extracting slab‑level features with a shared encoder and aggregating them through both permutation‑invariant set‑attention and bidirectional sequence‑attention branches. In five‑fold patient‑level cross‑validation, OAS‑MIL achieved a mean AUROC of 0.770, outperforming volumetric and MIL baselines, indicating that axial order provides a useful inductive bias for weakly supervised PNI prediction.
Inferring contrast enhancement from one pre-contrast breast MRI slice is underdetermined: post-contrast appearance contains physiological information that is not uniquely encoded in baseline anatomy. Optimizing only paired pixel fidelity can suppress uncertain lesion enhancement, whereas adversarial or stochastic generative objectives can favor realistic post-contrast appearance without guaranteeing patient-specific lesion fidelity.