Pith. sign in

REVIEW 18 cited by

Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2201.01266 v1 pith:AYI6YZVJ submitted 2022-01-04 eess.IV cs.CVcs.LG

Ali Hatamizadeh , Vishwesh Nath , Yucheng Tang , Dong Yang , Holger Roth , Daguang Xu This is my paper
classification eess.IVcs.CVcs.LG
keywords segmentationswinsemanticbrainsequencetransformertransformerstumors
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient and successively studying the progression of the malignant entity. In recent years, Fully Convolutional Neural Networks (FCNNs) approaches have become the de facto standard for 3D medical image segmentation. The popular "U-shaped" network architecture has achieved state-of-the-art performance benchmarks on different 2D and 3D semantic segmentation tasks and across various imaging modalities. However, due to the limited kernel size of convolution layers in FCNNs, their performance of modeling long-range information is sub-optimal, and this can lead to deficiencies in the segmentation of tumors with variable sizes. On the other hand, transformer models have demonstrated excellent capabilities in capturing such long-range information in multiple domains, including natural language processing and computer vision. Inspired by the success of vision transformers and their variants, we propose a novel segmentation model termed Swin UNEt TRansformers (Swin UNETR). Specifically, the task of 3D brain tumor semantic segmentation is reformulated as a sequence to sequence prediction problem wherein multi-modal input data is projected into a 1D sequence of embedding and used as an input to a hierarchical Swin transformer as the encoder. The swin transformer encoder extracts features at five different resolutions by utilizing shifted windows for computing self-attention and is connected to an FCNN-based decoder at each resolution via skip connections. We have participated in BraTS 2021 segmentation challenge, and our proposed model ranks among the top-performing approaches in the validation phase. Code: https://monai.io/research/swin-unetr

Discussion (0). Sign in to comment.

Forward citations

Cited by 18 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MS-DKC: A Dataset Knowledge Card Framework for Designing and Adapting Medical Image Segmentation Models

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    MS-DKC is a dataset knowledge card framework that maps image, morphology, supervision, context, and risk descriptors to design priors and failure modes, shown to produce dataset-specific model adaptations with improve...

  2. A Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    A multimodal 3D foundation model pretrained on LSM volumes via masked reconstruction and image-text alignment enables improved few-shot segmentation, classification, and deblurring.

  3. MedFlowSeg: Flow Matching for Medical Image Segmentation with Frequency-Aware Attention

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    MedFlowSeg is a conditional flow matching model for medical image segmentation that adds dual-branch spatial attention and frequency-aware attention to achieve more efficient inference than diffusion models while impr...

  4. Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    CATMIL augments nnU-Net with component-adaptive Tversky and MIL-based lesion supervision to raise Dice scores, small-lesion recall, and error control on the MSLesSeg dataset.

  5. Integrating Pathology and CT Imaging for Personalized Recurrence Risk Prediction in Renal Cancer

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Multimodal fusion of CT and pathology images improves recurrence risk prediction in kidney cancer, with the best model approaching the clinical Leibovich score.

  6. Differential-UMamba: Rethinking Tumor Segmentation Under Limited Data Scenarios

    cs.CV 2025-07 unverdicted novelty 6.0 of 10

    Diff-UMamba combines UNet with Mamba and adds signal differencing for noise reduction, yielding 1-3% segmentation gains on public medical datasets and 4-5% on a small internal lung cancer dataset under limited data co...

  7. AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans

    cs.CV 2026-07 conditional novelty 5.5 of 10

    A latent RSSM with hierarchical anatomical add/remove actions cuts HD95 by ~43% versus nnU-Net on fine-grained nested auricular CT segmentation.

  8. Anatomy-Guided Residual Motion Diffusion for Controllable 4D Cardiac MRI Synthesis

    cs.CV 2026-06 unverdicted novelty 5.0 of 10

    A semi-supervised VAE combined with static and residual motion LDMs generates anatomically consistent 4D cardiac MRI, achieving Pearson r > 0.8 controllability and 1.4% Dice improvement in downstream segmentation when...

  9. MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation

    eess.IV 2026-06 unverdicted novelty 5.0 of 10

    MLFFM-SegDiff adds a multi-level feature fusion module and dual-path encoder to a diffusion U-Net, reporting improved Jaccard (0.8546) and Dice (0.9207) scores over baselines on three skin lesion datasets.

  10. Beyond Instance-Level Self-Supervision in 3D Multi-Modal Medical Imaging

    cs.CV 2026-05 unverdicted novelty 5.0 of 10

    A self-supervised approach uses consistent spatial relationships of anatomical structures across patients to improve 3D multi-modal medical image representations, yielding modest gains on segmentation and classificati...

  11. Align then Refine: Text-Guided 3D Prostate Lesion Segmentation

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    A text-guided multi-encoder U-Net with alignment loss, heatmap calibration, and confidence-gated cross-attention refiner sets new state-of-the-art 3D prostate lesion segmentation performance on the PI-CAI dataset.

  12. SGP-SAM: Self-Gated Prompting for Transferring 3D Segment Anything Models to Lesion Segmentation

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    SGP-SAM transfers 3D SAM to lesion segmentation using a self-gated module for conditional multi-scale enhancement and a Zoom Loss, achieving 7.3% mDice gain over fine-tuning on MSD Liver Tumor data.

  13. A Space-Time Transformer for Precipitation Nowcasting

    cs.CV 2025-11 conditional novelty 5.0 of 10

    A full space-time attention video transformer recast as 64-class rainfall prediction with log-frequency class weighting won the Weather4Cast 2025 Cumulative Rainfall challenge (CRPS 3.135).

  14. Efficient Transformer-Based Localized Patch Sampling for Choroid Plexus Segmentation in Multiple Sclerosis

    cs.CV 2026-06 unverdicted novelty 4.0 of 10

    SwinUNETR model with 32x32x32 patch sampling achieves DSC of 0.868 for LVCP segmentation in MS, outperforming UXNET with 99% lower computation.

  15. AMO-ENE: Attention-based Multi-Omics Fusion Model for Outcome Prediction in Extra Nodal Extension and HPV-associated Oropharyngeal Cancer

    eess.IV 2026-04 unverdicted novelty 4.0 of 10

    An attention-based fusion model combining semi-supervised CT segmentation, radiomics, and clinical features predicts metastatic recurrence, overall survival, and disease-free survival in HPV+ oropharyngeal cancer with...

  16. EfficientGFormer: Multimodal Brain Tumor Segmentation via Pruned Graph-Augmented Transformer

    cs.CV 2025-08 reject novelty 4.0 of 10

    EfficientGFormer combines a pretrained nnFormer encoder with a dual-edge graph attention network and distillation to segment brain tumor subregions, claiming SOTA accuracy with lower compute.

  17. HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT

    cs.CV 2026-06 unverdicted novelty 3.0 of 10

    The HECKTOR 2025 challenge establishes performance benchmarks for multimodal PET/CT analysis of head and neck cancer, with top methods reaching Dice 0.75 on tumor segmentation, C-index 0.66 on recurrence-free survival...

  18. Why Relational Graphs Will Save the Next Generation of Vision Foundation Models?

    cs.CV 2025-08 conditional novelty 3.0 of 10

    A position paper arguing that vision foundation models need dynamic relational graphs for relational reasoning, with evidence drawn from the author's own prior action recognition and tumor segmentation systems.

Pith tools