arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
By Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
arXiv:2609.37848v1 Announce Type: cross
Abstract: Biomedical machine learning papers often compress model performance into one headline number. That number can look like a property of the model even...
By Bhanu Prakash Vangala, Sowmya Guda, Latha Peddi, Navya Vangala
arXiv:2607.18451v3 Announce Type: replace
Abstract: A foundation encoder is pretrained once on a large image corpus and then reused with its weights frozen. Each new task is solved by training a smal...
By Soroosh Tayebi Arasteh, Sven Nebelung, Daniel Truhn
arXiv:2608. 14705v1 Announce Type: cross Abstract: Hyperparameter optimization (HPO) can materially affect the performance of deep learning (DL) image classifiers, but there is little empirical guidance on how to derive the validation signal that drives it, especially for the small sample sizes common in fields such as medical imaging.
By Ljubomir Buturovic (East Palo Alto, United States)
arXiv:2607. 14984v1 Announce Type: new Abstract: Per-subgroup fairness audits of medical image classifiers face a sample-size problem: minority subgroups in held-out test sets have so few samples that the resulting confidence intervals on per-subgroup performance are wider than the bias the audit is meant to detect.
By Mahmoud Ibrahim, Bart Elen, Chang Sun, Gokhan Ertaylan, Michel Dumontier
The paper introduces the Semantic Tri-view Pipeline, an interpretable system that automatically screens teledermatology photographs for gradability by analyzing epidermal micro-relief across up to three smartphone views. It uses a lightweight DeepLabV3+ model to segment micro-relief fidelity and aggregates the resulting spatial masks with logistic regression, leveraging viewpoint redundancy to improve robustness. Evaluated on the SCIN dataset, the approach raises the AUC from 0.81 to 0.96 on optically clear cases, offering real‑time, privacy‑by‑design feedback to filter ungradable photo sets before clinician review.
By Robert Engel