The paper introduces the first adaptation of the MedSAM2 foundation model for interactive 3D segmentation of interstitial lung disease (ILD) on thoracic CT scans. It evaluates three fine‑tuning strategies and four prompt types—bounding‑boxes, points, lassos, and scribbles—finding that full model fine‑tuning yields the best performance, improving Dice scores by 4.7 percentage points over the baseline. A proof‑of‑concept workflow is presented where MedSAM2 is first initialized with an automatic prior and then refined by radiologist prompts, with all resources released on GitHub.
By Vasilis Dedousis, Lubnaa Abdur Rahman, Lorenzo Brigat{\omicron}, Ethan Dack, Andreas Christe, Christoph Frank, Manuela Funke-Chambour, Justus Roos, Adrian Huber, Lukas Ebner, Stavroula Mougiakakou
Purpose: To compare 2D, 2.5D, and 3D residual networks for pulmonary nodule malignancy-risk classification and evaluate momentum-contrast pretraining, mild augmentation, and exclusion of indeterminate...
The paper presents a pragmatic segmentation pipeline for brain metastases in the BraTS 2026 Task 1, using a 5‑fold nnU-Net ResEnc‑L ensemble trained for 1,000 epochs on 1,296 four‑modality cases. A rule‑based post‑processing cascade tuned for the lesion‑wise Dice similarity coefficient (LW‑DSC) improves performance, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 on enhancing tumour, tumour core, whole tumour, and resection cavity, respectively. The authors audit each post‑processing stage with a five‑fold out‑of‑fold analysis, confirm two stages as robust, and provide a mechanistic analysis of LW‑DSC, along with thirteen negative results that challenge common intuitions.
The paper presents a segmentation pipeline for brain metastases in both pre‑ and post‑treatment cases using a 5‑fold nnU‑Net ResEnc‑L ensemble trained on 1,296 four‑modality cases. A rule‑based post‑processing cascade improves the lesion‑wise Dice similarity coefficient (LW‑DSC) for enhancing tumour, tumour core, whole tumour, and resection cavity sub‑regions, achieving LW‑DSC scores of 0.733, 0.751, 0.713, and 0.549 respectively on the official validation leaderboard. The authors conduct a five‑fold out‑of‑fold analysis to validate the robustness of each post‑processing stage, provide a mechanistic explanation of LW‑DSC behaviour, and report thirteen negative results that challenge common intuitions, with all code released under Apache‑2.0.
By Haobin Liu, Xin Wang
The study compares 2D, 2.5D, and 3D residual networks for classifying pulmonary nodule malignancy risk using a retrospective LIDC‑IDRI cohort of 2,654 nodules. Models were trained with random initialization, momentum‑contrast pretraining, and mild data augmentation, and evaluated on patient‑disjoint splits. Augmented 2.5D models achieved the highest test Macro‑F1 (0.6537) and accuracy (0.6869), while 2D and 3D models performed lower, and pretraining generally reduced performance.
By Bahodir Nazarov, Zineb Gharib, Fatima Zazoul
The paper reports the submissions of team nucli to the HECKTOR 2026 challenge, which evaluates machine learning methods for head‑and‑neck cancer imaging. The team used self‑supervised pre‑trained encoders, training a 2D DINOv2 encoder on PET scans and combining a DynUNet with a SwinUNETR for segmentation, achieving a mean Dice of 0.5409. For TN staging, they fused geometric features from segmentation masks with PET‑DINOv2‑MIPs deep features via logistic regression, obtaining balanced accuracies of 0.3962 (T‑stage) and 0.6432 (N‑stage). For recurrence‑free survival prognosis, they fine‑tuned the PET‑DINOv2‑MIPs ViT‑S encoder with an MLP head under a CoxPH loss, achieving a Harrell's C‑index of 0.6577 and a weighted challenge score of 0.5802.
arXiv:2607. 01001v1 Announce Type: cross Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness.
By Nils Neukirch, Martin Maurer, Nils Strodthoff
arXiv:2609.01554v1 Announce Type: cross
Abstract: Automated lesion segmentation in whole-body PET/CT is complicated by the variety of physiological tracer uptake patterns and by the differing appeara...
By Marven Sherif (Brightskies), Amgad Elmasry (Brightskies), Youssef Ghazal (Brightskies), Ayman Elghotni (Brightskies)
arXiv:2606. 28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal.
By Jiasheng Wang, Tanun Jitwatcharakomol, Piyawadee Jongpradubgiat, Simeng Zhu
arXiv:2608.30844v1 Announce Type: cross
Abstract: Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to spa...
By Xinglong Liang, Chunyao Lu, Tianyu Zhang, Jiaju Huang, Tao Tan, Yunchao Yin, Lishan Cai
arXiv:2609.16775v1 Announce Type: new
Abstract: Annotating large radiology datasets is bottlenecked by the manual effort of delineating structures slice-by-slice in 3D volumes. Interactive methods re...
By Abhilaksh Singh Reen, Kushal Borkar, Ritvik Mahapatra
arXiv:2608.30021v1 Announce Type: cross
Abstract: Errors in radiology reports can adversely affect patient treatment, yet automated report quality assurance remains challenging because errors are oft...
By Hermione Warr, Harry Anthony, Lilli J Freischem, Yasin Ibrahim, Daniel R McGowan, Konstantinos Kamnitsas