Medical Image Segmentation

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

50 leaderboard tables shown for this task, 50 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. 10 shown of 50 until expanded.

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

52 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 52 until expanded.

Subtasks archive 2025-07-28

31 subtasks in the archive's task tree. 30 shown of 31 until expanded.

Most implemented papers archive 2025-07-28

30 shown of 1,080 papers with code (2,089 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.

• U-Net: Convolutional Networks for Biomedical Image Segmentation
  18 May 2015 487 repositories listed Syntology ran 510 of 757 samples · 247 unverified · 426 pointer-only (licence)
  There is large consent that successful training of deep networks requires many thousand annotated training samples.
• Densely Connected Convolutional Networks
  25 Aug 2016 146 repositories listed Syntology ran 18 of 71 samples · 53 unverified · 7 pointer-only (licence)
  Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output.
• SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
  2 Nov 2015 74 repositories listed Syntology ran 9 of 44 samples · 35 unverified · 10 pointer-only (licence)
  We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
• Attention U-Net: Learning Where to Look for the Pancreas
  11 Apr 2018 37 repositories listed Syntology ran 8 of 28 samples · 20 unverified · 7 pointer-only (licence)
  We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
• UNet++: A Nested U-Net Architecture for Medical Image Segmentation
  18 Jul 2018 34 repositories listed Syntology ran 5 of 28 samples · 23 unverified · 2 pointer-only (licence)
  Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
• V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
  15 Jun 2016 28 repositories listed Syntology ran 2 of 22 samples · 20 unverified
  Convolutional Neural Networks (CNNs) have been recently employed to solve problems from both the computer vision and medical image analysis fields.
• TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
  8 Feb 2021 22 repositories listed Syntology ran 6 of 7 samples · 1 unverified · 7 pointer-only (licence)
  Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
• Brain Tumor Segmentation with Deep Neural Networks
  13 May 2015 14 repositories listed
  Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN.
• UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
  11 Dec 2019 13 repositories listed Syntology ran 1 of 9 samples · 8 unverified · 2 pointer-only (licence)
  The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
• Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation
  16 Jul 2019 12 repositories listed Syntology ran 8 of 15 samples · 7 unverified
  We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information.
• 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…
• UNETR: Transformers for 3D Medical Image Segmentation
  18 Mar 2021 10 repositories listed
  Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence…
• nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation
  27 Sep 2018 9 repositories listed Syntology ran 3 of 11 samples · 8 unverified
  The U-Net was presented in 2015.
• Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation
  12 May 2021 7 repositories listed Syntology ran 3 of 18 samples · 15 unverified
  In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis.
• UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
  19 Apr 2020 7 repositories listed Syntology ran 4 of 17 samples · 13 unverified · 1 pointer-only (licence)
  UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
• Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
  1 Sep 2017 7 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core and enhancing…
• ResUNet++: An Advanced Architecture for Medical Image Segmentation
  16 Nov 2019 6 repositories listed Syntology ran 2 of 4 samples · 2 unverified · 4 pointer-only (licence)
  Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer.
• Automated Design of Deep Learning Methods for Biomedical Image Segmentation
  17 Apr 2019 6 repositories listed
  Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
• MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation
  11 Feb 2019 6 repositories listed Syntology ran 1 of 7 samples · 6 unverified
  We have compared our proposed architecture MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images.
• A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation
  18 Oct 2018 6 repositories listed
  We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation.
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
  25 Apr 2023 5 repositories listed
  In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation.
• Heuristic Dropout: An Efficient Regularization Method for Medical Image Segmentation Models
  23 May 2022 5 repositories listed
  This manuscript goes deep into the research of the Dropout algorithm, which is commonly used in neural networks to alleviate the overfitting problem.
• Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation
  8 Feb 2021 5 repositories listed
  We compare our loss function performance against six Dice or cross entropy-based loss functions, across 2D binary, 3D binary and 3D multiclass segmentation tasks, demonstrating that our proposed loss function is robust…
• Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
  31 Dec 2020 5 repositories listed
  In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task.
• Boundary loss for highly unbalanced segmentation
  17 Dec 2018 5 repositories listed Syntology ran 2 of 26 samples · 24 unverified
  We propose a boundary loss, which takes the form of a distance metric on the space of contours, not regions.
• VM-UNet: Vision Mamba UNet for Medical Image Segmentation
  4 Feb 2024 4 repositories listed Syntology ran 11 of 17 samples · 6 unverified · 2 pointer-only (licence)
  To our best knowledge, this is the first medical image segmentation model constructed based on the pure SSM-based model.
• U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
  29 Nov 2023 4 repositories listed
  We evaluate our method on several public medical image segmentation datasets for skin lesion segmentation and polyp segmentation, and the experimental results demonstrate the segmentation accuracy of our new method over…
• AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
  16 Jun 2022 4 repositories listed
  Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the…
• WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
  3 Nov 2021 4 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale…
• SinGAN-Seg: Synthetic training data generation for medical image segmentation
  29 Jun 2021 4 repositories listed
  The pipeline is evaluated using qualitative and quantitative comparisons between real and synthetic data to show that the style transfer technique used in our pipeline significantly improves the quality of the generated…

Syntology lines on 19 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.