arXiv Computer Vision By Moein Heidari, Junbo Rao, Jai Choraria, Wenjin Chen, David J. Foran, Ilker Hacihaliloglu

Which Pretext Task Transfers? Self-Supervised Pretraining Objectives for Lung Ultrasound

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arXiv AI
Jul 23

Self-supervision drives representational convergence in medical foundation models more than clinical supervision

arXiv:2607. 20274v1 Announce Type: cross Abstract: Medical image encoders from different groups are increasingly treated as interchangeable, on the assumption that scale and clinical supervision concentrate their representations onto a shared structure.

By Soroosh Tayebi Arasteh, Sebastian Ziegelmayer, Mahshad Lotfinia, Lisa Adams, Sven Nebelung, Jakob Nikolas Kather, Daniel Truhn
arXiv AI
Sep 11

OmniMed-FL: A Robust Multimodal Federated Learning Framework for Clinical Diagnosis

OmniMed‑FL is a multimodal federated learning framework that fuses chest radiographs and synthetic patient notes to classify five clinical conditions. The study benchmarks eight fusion strategies, three initializations, and four missing‑text imputation rules across 3–20 hospital clients under non‑IID Dirichlet partitioning, showing that federated approaches (FedAvg, FedProx, SCAFFOLD‑AdamW) outperform local‑only training. Multimodal fusion consistently improves performance, achieving macro‑F1 scores up to 0.956 on the synthetic corpus and 0.906 on the radiograph corpus.

By Ayush Debnath, Ruelia Saha, Sudip Misra
arXiv Machine Learning
Jul 27

IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data

arXiv:2607. 22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.

By Masashi Sode, Gianmarco Pinton
arXiv Computer Vision
Aug 28

DALE-CT: Depth-Aware 2D Slice Encoders Learn an Anatomical World Model of Chest CT

DALE-CT introduces depth‑aware 2D slice encoders that learn an anatomical world model of chest CT scans without 3D or positional supervision. By sampling self‑supervised views across a physical $z$‑axis slab, the encoder captures how anatomy changes between neighboring slices, enabling it to recover slice ordering and distinguish slices by anatomy alone. The model, trained on a large 287k‑scan corpus, achieves state‑of‑the‑art performance on CT‑RATE and is released with full code and evaluation tools.

By Evan W. Damron, Mahmut S. Gokmen, Mitchell A. Klusty, Caroline N. Leach, Emily B. Collier, V. K. Cody Bumgardner