The paper introduces AMPLIFAI, the first public dataset of multiphase abdominal CT scans annotated with LI-RADS categories and segmented for three key LI-RADS features: arterial phase hyperenhancement, washout, and enhancing capsule. It outlines the dataset’s composition, curation process, and annotation pipeline following the Datasheets for Datasets format to promote transparency and reproducibility. The dataset aims to support the development of AI models for automated hepatocellular carcinoma diagnosis using the biopsy‑free, imaging‑based LI‑RADs framework.
By Pranav Kulkarni, Nikhil Shah, Amritansh Suryavanshi, Jana Delfino, James Tonascia, Jade Wong-You-Cheong, Barton Lane, Joseph Chirico, Jeffrey D. Hirsch, Ang Li, Heng Huang, Florence X. Doo
arXiv:2610.18243v1 Announce Type: cross
Abstract: Given the growing global burden of liver tumors and diffuse liver diseases, non-contrast computed tomography (NCCT) is increasingly used for opportun...
By Hao Zhang, Yi Huang, Wei Liu, Yuan Gao, Zeju Li, Xu Han, Yu Shi, Ling Zhang, Ke Yan
LiFUSE introduces a unified frequency‑spatial self‑supervised pretraining framework for liver disease analysis on non‑contrast CT scans. The method employs a frequency branch that uses random band‑stop masking and spectrum reconstruction, and a spatial branch that performs hybrid‑granularity masked volumetric reconstruction, with a cross‑domain consistency scheme to fuse the two branches. Experiments on three clinical tasks and a public transfer setting demonstrate that LiFUSE consistently outperforms strong baselines, indicating its potential to enhance liver disease screening using low‑contrast NCCT images.
arXiv:2607. 03253v1 Announce Type: cross Abstract: As hematoxylin & eosin (H&E) staining constitutes the primary entry point in routine diagnostic workflows, computer-aided diagnosis from whole-slide H&E images is of particular clinical relevance.
By Ivica Kopriva, Dario Sitnik, Arijana Pacic, Karolina Krstanac, Irena Veliki Dalic, Marijana Popovic Hadzija
The paper presents a systematic evaluation of six unsupervised image‑to‑image generative models for virtual Sirius Red staining of mouse liver tissue from routine H&E slides. It benchmarks these models across 54 scaling configurations on a newly released paired H&E‑to‑SR dataset, assessing perceptual, distributional, and task‑specific performance, and further trains the best models into deep ensembles to quantify epistemic uncertainty. The study finds that GAN‑based and diffusion‑based methods differ markedly across these metrics, indicating that reliable virtual staining requires reporting and selecting on all three axes simultaneously.
By Qasim Siddiqui, Adrian Friebel, Maiju Myllys, Zaynab Hobloss, Daniela Gonzalez, Ahmed Ghallab, Stefan Hoehme
This study investigates deep learning and explainable AI methods to diagnose hepatocellular carcinoma (HCC) and identify diagnostic and prognostic biomarkers across five disease stages using a transcriptomic dataset built via semi‑supervised learning. The best model used 15 genes selected by SelectKBest, achieving 90.74% accuracy, while a 20‑gene model had the lowest loss of 0.3187. SHAP‑based XAI highlighted DNAJB14 as the most influential gene, and functional validation showed that inhibiting DNAJB14 reverses key malignant traits of HCC cells.
By Ali Bou Nassif, Darko Castven, Manar Abu Talib, Jibran Sualeh Muhammad, Ahmed Ammar Kubba, Jens Marquardt, Abdalla Sayed Ali
The study introduces RCC-Align, a cross‑modal contrastive learning framework that aligns paired histopathology whole‑slide images and CT scans to enhance noninvasive grading of clear cell renal cell carcinoma (ccRCC). Using patient‑level five‑fold cross‑validation on TCGA and CPTAC cohorts, RCC‑Align achieved an AUC of 0.601 and AUPRC of 0.599 for low‑ versus high‑grade ccRCC classification, outperforming CT‑only baselines and showing stronger WSI‑CT embedding alignment. The approach relies solely on CT at inference, potentially aiding grading when biopsy is unsafe or limited by tumor heterogeneity.
By Amit Das, Tanmay Shukla, Naofumi Tomita, Faraz Farhadi, Jessica Sin, Ari Hakimi, Chad Vanderbilt, Jie-Fu Chen, Ritesh Kotecha, Weijie Ma, Bing Ren, Saeed Hassanpour
arXiv:2607.12750v3 Announce Type: replace
Abstract: Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximatel...
By Elham Amjadi, Amin Bahreini, Sayed Mohammad Hasan Emami, Sayyed Mohammadreza Hakimian, Alireza Fahim, Hojjatollah Rahimi, Hamidreza Bolhasani
arXiv:2610.34599v1 Announce Type: new
Abstract: Despite advances in automated liver lesion segmentation, a gap remains between image-derived predictions and clinically interpretable radiology reports...
By Fatma Ahmed Mansour, Rahatara Ferdousi, Manar Fathy Elshenawy
Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths globally, with approximately 1,926,425 new cases and 904,019 deaths reported in 2022. Accurate histologic grading plays a critical role in prognosis and treatment planning for colorectal adenocarcinoma.
arXiv:2609.14010v1 Announce Type: new
Abstract: We present CirrGuide, a deep cascaded framework for cirrhotic liver segmentation and severity classification. Cirrhosis causes progressive structural c...
By Muntaqim Ahmed Raju, Ruizhe Ma
arXiv:2602. 02124v2 Announce Type: replace-cross Abstract: Drug-induced toxicity is a leading cause of preclinical and early-clinical failure, making early detection critical.
By Olga Graf, Dhrupal Patel, Peter Gro{\ss}, Charlotte Lempp, Matthias Hein, Fabian Heinemann