arXiv:2608. 07857v1 Announce Type: cross Abstract: Foundation models provide transferable CT representations, but predictions based directly on these embeddings are difficult to interpret.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2609.38271v1 Announce Type: new Abstract: Morphological characteristics such as spiculation and lobulation play an important role in assessing pulmonary nodules on computed tomography (CT), par...
arXiv:2609.26578v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced CT remains clinically challenging because clear cell...
Background: Early prediction of distant metastasis (DM) risk in head and neck cancer (HNC) can enable timely interventions that may improve treatment outcomes. Many current machine learning methods rely on prior knowledge of the region of interest such as tumor segmentations, which require expert knowledge, is time-consuming and introduces user-dependent variability.
FedHisto-PAST v2 is a parameter‑efficient, stain‑aware federated learning framework for cross‑site lung histopathology classification, combining a frozen HIBOU‑B foundation model with techniques such as paired‑view prediction, feature consistency, prototype learning, and adaptive aggregation. In a five‑client, non‑IID simulation and an exploratory LungHist700 cohort, the method achieved a Macro‑F1 of 0.7286 and a balanced accuracy of 0.7305, with the prediction‑level consistency component providing the most clear independent benefit. The framework updated only about 1.25% of the model parameters, demonstrating efficient adaptation while acknowledging limitations in privacy guarantees and clinical validation.
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.
arXiv:2607. 12075v1 Announce Type: cross Abstract: Background: Deep learning models can classify thyroid nodules on ultrasound, but reliable clinical decision support also requires calibrated probabilities, uncertainty estimation, and selective referral, particularly under dataset shift.