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:2403. 05532v2 Announce Type: replace Abstract: We introduce Tune without Validation (Twin), a simple and effective pipeline for tuning learning rate and weight decay of homogeneous classifiers without validation sets, eliminating the need to hold out data and avoiding the two-step process.
By Lorenzo Brigato, Stavroula Mougiakakou
arXiv:2608. 16198v1 Announce Type: cross Abstract: Dermatology models face distribution shifts in teledermatology settings, where submitted images differ from the training data in lighting, angle, distance, focus, and framing.
By Fabian Gr\"oger, Marco Weishaupt, Philippe Gottfrois, Simone Lionetti, Linda Wermelinger, Nipun Ranasekara, Ludovic Amruthalingam, Alexander A. Navarini, Marc Pouly
arXiv:2610.01284v1 Announce Type: cross
Abstract: Model validation estimates the performance of a complete learning procedure on new data. However, an invalid split can produce an optimistic and stab...
By Mehmet Baygin, Sengul Dogan, Turker Tuncer
arXiv:2608. 28063v1 Announce Type: new Abstract: Multi-organ ultrasound classifiers increasingly combine attention, mixture-of-experts routing, uncertainty gating, and evidential deep learning (EDL) objectives to address heterogeneous anatomy and acquisition.
By Yang Song, Pengbo Sun, Shichang Feng, Ye Zhu, Xin Xu, Ziran Wang
arXiv:2607. 16888v1 Announce Type: cross Abstract: Deploying a medical imaging model that must later accommodate a modality it has never seen is a recurring practical problem: retraining the shared representation is expensive and destroys performance on the modalities already in service.
By Ranat Das Prangon, Istiaque Ahmed, Shajid Hasan Naim, Waseem Mustak Zisan, Hossain Md Shakhawat