# PAN: Projective Adversarial Network for Medical Image Segmentation Source: [PAN: Projective Adversarial Network for Medical Image Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:38:30.146Z Authors: Naji Khosravan, Aliasghar Mortazi, Michael Wallace, Ulas Bagci Published: 2019-06-11T03:51:09.000Z ## Abstract Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open problem. In this study, we address this computational burden by proposing a novel projective adversarial network, called PAN, which incorporates high-level 3D information through 2D projections. Furthermore, we introduce an attention module into our framework that helps for a selective integration of global information directly from our segmentor to our adversarial network. For the clinical application we chose pancreas segmentation from CT scans. Our proposed framework achieved state-of-the-art performance without adding to the complexity of the segmentor. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()