arXiv:2607. 02768v1 Announce Type: cross Abstract: Pathologic complete response and tumor shrinkage measure whether breast cancer responds to neoadjuvant therapy, but not whether that response was structurally favorable, persistent, or hidden beneath volume loss.
arXiv:2609.26105v1 Announce Type: cross Abstract: Prediction of pathological complete response before neoadjuvant chemotherapy may facilitate more tailored therapeutic planning for breast cancer pati...
arXiv:2608. 03145v1 Announce Type: new Abstract: Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain largely obscured.
arXiv:2606. 29977v1 Announce Type: cross Abstract: Objectives: To characterize residual false positives in prostate MRI detection, and to evaluate a lightweight post-hoc refinement head for case-level specificity.
arXiv:2603. 01250v2 Announce Type: replace-cross Abstract: Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality.
arXiv:2609.26578v1 Announce Type: new Abstract: Accurate preoperative subtype classification of renal cell carcinoma (RCC) from contrast-enhanced CT remains clinically challenging because clear cell...
The paper introduces SWIFT, a Swin V2‑based model pretrained on 10,444 3D CT volumes and fine‑tuned for rectal cancer segmentation on T2‑weighted MRI. Four configurations—full fine‑tuning (SWIFT), decoder compression (SWIFTe), low‑rank adaptation (SWIFTe‑LoRA), and a LoRA‑decoder ensemble (SWIFTe‑LDE4)—were evaluated on 247 cases, showing that SWIFTe reduces parameters by 70.1% while improving tumor detection and radiomic agreement. The study also demonstrates a trade‑off between detection and boundary agreement, and highlights that SWIFTe‑LDE4 achieves the lowest calibration errors after temperature scaling.
arXiv:2608.24688v1 Announce Type: new Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal ob...
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%).
Objectives: To characterize residual false positives in prostate MRI detection, and to evaluate a lightweight post-hoc refinement head for case-level specificity. Materials and Methods: This retrospective study used PI-CAI (5-fold cross-validation) and Prostate158 (n=158; external).
arXiv:2512. 22262v2 Announce Type: replace-cross Abstract: Routine histology contains rich prognostic information in stage II/III colorectal cancer, much of which is embedded in complex spatial tissue organisation.
arXiv:2605.05522v3 Announce Type: replace-cross Abstract: Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities s...