Lung Nodule Segmentation

Benchmarks archive 2025-07-28

5 leaderboard tables shown for this task, 5 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

15 shown of 15 papers with code (30 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Road Extraction by Deep Residual U-Net
  29 Nov 2017 13 repositories listed
  Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis.
• Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
  20 Feb 2018 12 repositories listed Syntology ran 5 of 8 samples · 3 unverified · 5 pointer-only (licence)
  In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net…
• Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
  31 Aug 2019 4 repositories listed
  To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
• Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention
  24 Oct 2021 2 repositories listed
  With an Uncertainty-Aware Module, this network can provide a Multi-Confidence Mask (MCM), pointing out regions with different segmentation uncertainty levels.
• Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
  14 Jul 2020 2 repositories listed
  To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a…
• Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
  19 Aug 2019 2 repositories listed
  More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including…
• A Unified Multi-Scale Attention-Based Network for Automatic 3D Segmentation of Lung Parenchyma & Nodules In Thoracic CT Images
  23 May 2025 1 repository listed
  It can be seen from the results that the proposed method achieves better performance than state-of-the-art methods.
• Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
  21 Feb 2025 1 repository listed
  To address the data scarcity in lung cancer screening, we propose Lung-DDPM, a thoracic CT image synthesis approach that effectively generates high-fidelity 3D synthetic CT images, which prove helpful in downstream lung…
• INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses
  5 Sep 2023 1 repository listed
  In this study, we propose a novel deep neural network (DNN), entitled InceptNet, in the scope of medical image processing, for early disease detection and segmentation of medical images in order to enhance precision and…
• AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
  28 Jul 2023 1 repository listed
  Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinical applications with limited annotations.
• Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data
  1 May 2023 1 repository listed
  To this end, we have developed a 3D probabilistic segmentation framework augmented with NFs, to enable capturing the distributions of various complexity.
• Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism
  15 Mar 2023 1 repository listed
  Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty.
• U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation
  20 Mar 2020 1 repository listed
  Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images.
• Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation
  8 Oct 2019 1 repository listed
  Approach: We introduce an extension of the standard level set image segmentation method where the velocity function is learned from data via machine learning regression methods, rather than a priori designed.
• iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
  30 Nov 2018 1 repository listed
  We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.