arXiv:2606. 11144v1 Announce Type: new Abstract: Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computational models on the corresponding longitudinal patient trajectories.
By Abhijoy Sarkar, Aarchi Singh Thakur
arXiv:2608. 05359v1 Announce Type: new Abstract: CASCADE is an agentic framework that predicts downstream transcriptional effects of gene perturbation from precomputed ARACNe regulatory networks, exposed via MCP.
By Jose A. Bird
The paper presents an optimal‑transport based generative model that learns the distributional differences between healthy and diseased patients, producing per‑patient counterfactuals and label‑free attribution heatmaps. On tabular breast cancer data the model achieves high malignancy scoring (AUROC ≈ 0.91) and its attributions correlate moderately with a supervised classifier, yet it does not surpass logistic regression. In chest X‑ray experiments the transport heatmaps capture population‑level signals but fail to localize real lesions, revealing a synthetic‑to‑real gap that challenges the reliability of label‑free explanations.
By Lalit Kumar
TAM-Chain is a multi‑scale thyroid cytology classification framework that uses Absorbing Markov Chains and Shannon Entropy to quantify uncertainty and dynamically decide when to stop processing and refer to a specialist. It processes images at 10×, 20×, and 40× magnifications, achieving a Macro F1 score of 0.9741 on an internal test set with a 0 % false‑negative rate, and maintains a Macro F1 of 0.7026 on an external validation set with severe domain shift. The method outperforms single‑magnification baselines by adaptively adjusting stopping steps and triggering specialist referrals, thereby reducing critical diagnostic errors.
By Hai Pham Ngoc
arXiv:2606. 17491v1 Announce Type: cross Abstract: Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors.
By Adolphus Wagala, Mehmet Samur, Giovanni Parmigiani
The study examined whether embeddings from four foundation models—Mammo-CLIP, HOPPR, MedImageInsight, and BiomedCLIP—could detect pre‑diagnostic changes in screening mammograms. Using 1,773 biopsied women and matched controls, the researchers measured the speed of movement along a data‑derived “cancer direction” in embedding space over successive screening intervals. They found that embeddings from clinically grounded models (Mammo‑CLIP, HOPPR, MedImageInsight) showed faster drift in malignant cases compared to controls, while the general biomedical model BiomedCLIP did not, indicating that foundation model embeddings can encode early tissue changes without task‑specific fine‑tuning.
By Kalina P. Slavkova, Eric Brattain, Aditya Gowd, Akash Pattnaik, Jean-Benoit Delbrouck, Matthew Morgan, Julie Bauml, Javid Abderezaei, Khan Siddiqui