The study introduces a gaze-informed transformer framework that classifies radiologist expertise during thoracic CT interpretation by integrating eye‑tracking data into volumetric feature learning. Using a DINOv2 backbone, the model incorporates a learnable log‑space bias in self‑attention and gaze‑weighted pooling of patch embeddings. Trained on 182 CT reading sessions from five radiologists, it achieved an ROC‑AUC of 0.91 and an F1 score of 0.86, outperforming adapted baseline methods.
By Leila Khaertdinova, Anna Anikina, Claudia Mello-Thoms, Bulat Ibragimov
The paper introduces the Cross‑Modal Triage Network (CMTN), a multimodal deep‑learning model that fuses a Swin Transformer V2 visual encoder with a PubMedBERT text encoder to perform severity‑based triage, pathology detection, and generate visual explanations for chest radiographs. Trained on 34,639 image‑text pairs from MIMIC‑CXR‑JPG, the CMTN achieves high ordinal agreement with reference labels (QWK = 0.9341) and excellent pathology detection (macro‑AUROC = 0.9970) while operating with 34 ms latency. However, a blinded clinical audit revealed low agreement with expert radiologists (QWK = 0.1399) and only modest spatial‑semantic concordance in heatmaps, underscoring the gap between algorithmic performance and clinical judgment.
By Zinah Ghulam, Richa Mittal, Eranga Ukwatta
arXiv:2608. 03890v1 Announce Type: cross Abstract: A clinically useful chest X-ray system must go beyond fluent report generation: it should classify findings with tunable decision thresholds, localize them spatially, and derive the anatomical measurements upon which many diagnoses depend.
By Mercy Prasanna Ranjit, Anirban Porya, Sathvik Joel, Niharika Vadlamudi, Nikhilesh Chowdary Eathamukkala, Prasanth V V, Abhyuday Kumara Swamy, Pranay Narhari Umredkar, Pradeep Narayan, Vivek Rajagopal, Tanuja Ganu
arXiv:2610.28289v1 Announce Type: new
Abstract: Radiologists interpret follow-up chest X-rays by comparing current and prior examinations to identify new, resolved or progressing findings. Most repor...
By Sike Xiang, Shuang Chen, Julie Cox, Amir Atapour-Abarghouei
arXiv:2603. 06697v2 Announce Type: replace-cross Abstract: Vision--language models (VLMs) process images as visual tokens, yet their intermediate reasoning is often carried out in text, which can be suboptimal for visually grounded radiology tasks.
By Yiwei Li, Yifan Zhou, Huaqin Zhao, Zihao Wu, Zhengliang Liu, Xiang Li, Quanzheng Li, Tianming Liu, Lin Zhao
Current evaluation protocols for Vision-Language Models (VLMs) in Radiology Report Generation (RRG) rely on report-level metrics that measure lexical overlap or aggregate clinical correctness. However, such metrics do not test whether individual diagnostic statements stem from the actual pathological evidence visible in the image.