most citedMultimodal Large Language Model driven Radiology Report Generation with Clinical Knowledge Enhancement

1 citations · 1 across the 1 of their papers we have counts for

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5 papers

cs.CV20261 cited

Multimodal Large Language Model driven Radiology Report Generation with Clinical Knowledge Enhancement

Miaojing Shi, Tianyu Cen, Zijie Yue +3

Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. The performance of current RRG approaches remains…

cs.CV2026

Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge

Tobias Rueckert, David Rauber, Raphaela Maerkl +58

Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minim…

cs.CV2026

Where It Moves, It Matters: Referring Surgical Instrument Segmentation via Motion

Meng Wei, Kun Yuan, Shi Li +7

Enabling intuitive, language-driven interaction with surgical scenes is a critical step toward intelligent operating rooms and autonomous surgical robotic assistance. However, the…

cs.CV2025

Grounding Surgical Action Triplets with Instrument Instance Segmentation: A Dataset and Target-Aware Fusion Approach

Oluwatosin Alabi, Meng Wei, Charlie Budd +2

Understanding surgical instrument-tissue interactions requires not only identifying which instrument performs which action on which anatomical target, but also grounding these inte…

cs.CV2025

SurgPIS: Surgical-instrument-level Instances and Part-level Semantics for Weakly-supervised Part-aware Instance Segmentation

Meng Wei, Charlie Budd, Oluwatosin Alabi +2

Consistent surgical instrument segmentation is critical for automation in robot-assisted surgery. Yet, existing methods only treat instrument-level instance segmentation (IIS) or p…