arXiv:2608.24486v2 Announce Type: replace-cross
Abstract: Reliable clot-volume quantification and subsequent risk assessment in pulmonary embolism depend on precise segmentation of emboli on computed...
By Qihang Sun, Zhongxiao Liu, Bailiang Jian, Shenman Qiu, Jingyuan Wang, Lei Zhang, Lixiang Xie, Jiazhen Pan, Christian Wachinger
The study evaluates how well different medical foundation models agree on image embeddings by measuring mutual k‑nearest‑neighbor overlap across 750,000 images from 14 datasets and 18 public models. Results show that label‑supervised models converge the least, sharing only 6–10% of nearest neighbors, whereas models trained with self‑distillation, masked image modeling, or contrastive pretraining share up to 80%. The authors suggest that developers can select pretraining objectives to control convergence and recommend testing unlabeled images for nearest‑neighbor consistency before replacing a model.
By Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia, Lisa Adams, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
Med-AR introduces two autoregressive vision‑language models, Med‑AR‑8B and Med‑AR‑2B, pretrained on structured radiology reports, abnormality‑focused text, and region annotations to address long‑tailed chest X‑ray classification. The models outperform existing contrastive, self‑supervised, and supervised encoders—including Med‑CLIP, CheXFound, EVA‑Base, ARK, and BioViL‑T—across PadChest, MIMIC‑CXR, and CheXpert, achieving higher mean AUROC and AUPRC for head, medium, and tail findings and lower excess area under the risk‑coverage curve. Med‑AR also demonstrates improved selective‑prediction performance, with Med‑AR‑8B raising tail‑label mean AUPRC on MIMIC‑CXR from 0.1033 to 0.1441 and Med‑AR‑2B delivering the strongest discrimination on PadChest.
By Janhavi Prabhu, Sahil, Akshay V, Shivam Shukla, Manoj Tadepalli, Preetham Putha
arXiv:2608.29348v1 Announce Type: new
Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...
By Jakob Wasserthal, Joshy Cyriac, Michael Bach, Kimia Mozahheb Yousefi, Minh-Son To, M\'at\'e Sik, C\'edric H\'emon, Thomas Weikert, Martin Segeroth
The study investigates how image‑domain Poisson perturbations affect the classification of ischemic core/penumbra in non‑contrast CT slices. Using a CPAISD cohort, the authors compared direct ResNet‑18 classification with a denoising‑then‑classifying pipeline and found that denoising generally reduced performance. A prospective experiment with joint denoising‑classification models showed no statistically significant advantage over direct noisy classification, indicating limited robustness to Poisson noise.
By Rhea Ghosal, Ronok Ghosal, Eileen Lou
arXiv:2609.37750v1 Announce Type: cross
Abstract: Pulmonary embolism (PE) is a leading cause of cardiovascular mortality, yet the real-world performance of FDA-cleared AI detection models remains inc...
By Aawez Mansuri, Mohammadreza Chavoshi, Theodorus Dapamede, Wasif Bala, Beatrice Brown-Mulry, Rohan Isaac, Bardia Khosravi, Hanzhou Li, Frank Li, John T. Moon, Chad Robichaux, Dan I. G. Cohen-Addad, Ninad V. Salastekar, Janice Newsome, Judy W. Gichoya, Hari Trivedi