# RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget Source: [RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:00:51.913Z Authors: Sourajit Saha, Shaswati Saha, Md Osman Gani, Tim Oates, David Chapman Published: 2023-02-13T06:52:47.000Z ## Abstract Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN)architectures with downstream and upstream propagation flow are popular for segmentation in medical diagnosis. However, due to performing spatial downsampling and upsampling in multiple stages, information loss is inexorable. On the contrary, connecting layers densely on high spatial resolution is computationally expensive. In this work, we devise a Loose Dense Connection Strategy to connect neurons in subsequent layers with reduced parameters. On top of that, using a m-way Tree structure for feature propagation we propose Receptive Field Chain Network (RFC-Net) that learns high resolution global features on a compressed computational space. Our experiments demonstrates that RFC-Net achieves state-of-the-art performance on Kvasir and CVC-ClinicDB benchmarks for Polyp segmentation. ## 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]()