arXiv:2609.16597v1 Announce Type: cross Abstract: Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapp...
The paper introduces NeuroFusion, an assistive brain‑MRI report generator that surfaces latent tumor signals from a frozen Mistral‑7B backbone. By adding discriminative field‑classifier heads over per‑lesion features, NeuroFusion restores accurate diagnoses (meningioma 0.92, metastasis 0.75) and improves prose quality while reducing latency 5–6×. A controlled negative result shows that overriding the decoder with a learned diagnosis pin harms performance, and grammar‑constrained decoding yields high schema‑validity (92.3%).
Background Non-invasive presurgical diagnosis of brain tumor types from Magnetic Resonance Imaging (MRI) is essential but challenging due to overlapping imaging features across tumor types, inter-obse...
arXiv:2609.25088v1 Announce Type: cross Abstract: Survival prediction for glioblastoma multiforme (GBM) demands models that are both accurate and interpretable, yet existing approaches treat these ob...
OpenMTB‑Audit is an open‑source benchmark that tests large language models on 500 synthetic non‑small cell lung cancer cases, covering five adversarial error categories and four safety labels: Supported, Partially Supported, Unsupported, and Insufficient Information. The study found that all eight tested LLMs over‑refused Partially Supported recommendations, collapsing labels to achieve high safety scores. A deterministic seven‑module framework, MTB‑AuditAgent, was introduced to reduce over‑refusal to 6.7% and reach 91.2% accuracy, while an oncologist annotation study highlighted disagreement around the boundary between information sufficiency and treatment optimization.
arXiv:2609.39429v1 Announce Type: cross Abstract: Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ in a mult...
arXiv:2606. 30313v1 Announce Type: cross Abstract: Longitudinal glioblastoma response assessment requires comparing subtle tumor changes across MRI time points using structured clinical criteria such as RANO.
CFB-GBM v2.0 is an expanded longitudinal dataset of 264 glioblastoma patients, providing complete Gross Tumour Volume (GTV) delineations across all timepoints and derived volumetric RANO 2.0 response labels. The dataset includes brain masks, pre‑computed radiomic features, and WHO classification guidelines, all validated by radiation oncologists. It is publicly available on TCIA for use in computational methods for treatment response prediction and disease progression modeling.
arXiv:2606. 28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal.
arXiv:2608.22085v1 Announce Type: new Abstract: Synthetic clinical data generation with large language models addresses the scarcity that limits cancer staging research, but oncology hallucinations a...
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.
arXiv:2607. 16317v1 Announce Type: cross Abstract: Deep networks now subtype brain tumors on MRI about as well as specialist readers, yet accuracy is not what keeps them out of the clinic.