The paper introduces Variational Risk Minimization (VRM), a distillation framework that treats large vision‑language model (LVLM) report variants as Monte Carlo samples of latent clinical interpretations. VRM learns from a variationally marginalized teacher distribution, providing uncertainty‑aware supervision even when modalities are missing, and outperforms fine‑tuning baselines while improving calibration. In a compact edge‑student setup, a confidence‑gated cascade achieves an AUC of 0.941 at 103 ms latency with only 20.3 % cloud escalation, offering a clear reliability‑latency trade‑off for cloud‑edge medical imaging workflows.
arXiv:2608. 14766v1 Announce Type: cross Abstract: Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irreducible data ambiguity.
arXiv:2607. 02998v1 Announce Type: cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.
arXiv:2506.10633v2 Announce Type: replace Abstract: Latent Diffusion Models have shown remarkable results in text-guided image synthesis in recent years. In the domain of natural (RGB) images, recent...
arXiv:2607. 02998v2 Announce Type: replace-cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.
arXiv:2607. 06633v1 Announce Type: cross Abstract: In this paper, we address the problem of multimodal federated learning with missing modality.
arXiv:2605. 18419v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) can couple visual perception with open-ended clinical reasoning, making them attractive for computational histopathology.
arXiv:2608. 20305v1 Announce Type: new Abstract: Myocardial scar segmentation from single-stack late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) imaging has been a longstanding and clinically important challenge, particularly in the presence of low tissue contrast, diffuse, and small scar regions.
The paper introduces MedCertAIn, a framework that uses data‑driven priors to enhance uncertainty estimation in multimodal clinical models. By integrating cross‑modal similarity and modality‑specific corruptions into neural network priors, the authors improve predictive performance and selective prediction for in‑hospital mortality risk using MIMIC‑IV and MIMIC‑CXR data. The results demonstrate competitive accuracy and notable gains over deterministic and stochastic baselines, underscoring the potential of such priors for reliable, uncertainty‑aware clinical decision support.
The paper introduces PRISM, a Compositional Reward Model framework that decomposes image quality into multiple verifier‑grounded stages for conditional medical image generation. By assigning distinct rewards for fine‑to‑coarse properties—such as intensity, texture, structural alignment, and semantic fidelity—and combining them via a Hierarchical Constrained Propagation mechanism, PRISM addresses shortcomings of single‑scalar reward approaches. Experiments on PanNuke, CeDeM, and ISIC datasets show that data generated with PRISM improves downstream model performance, achieving higher mDice, lower MRE, and increased F1 scores compared to baseline methods.
arXiv:2609.23876v1 Announce Type: new Abstract: Clinical multimodal models must often predict before all chest X-ray (CXR) and electronic health record (EHR) inputs are available. Existing approaches...
arXiv:2606. 01537v1 Announce Type: cross Abstract: Clinical diagnosis often requires combining imaging with physiological measurements, yet deployed models typically operate on unimodal data.
arXiv:2609.26384v1 Announce Type: new Abstract: Medical image interpretation is high-volume and time-consuming, and while AI interpretation can reduce workload, fully autonomous deployment carries po...