mlsanityData to 14 Oct 2026

A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation

arXiv 1810.07842 ↗First posted 18 Oct 2018cs.CVcited by 172

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Abstract (arXiv)

We propose a generalized focal loss function based on the Tversky index to address the issue of data imbalance in medical image segmentation. Compared to the commonly used Dice loss, our loss function achieves a better trade off between precision and recall when training on small structures such as lesions. To evaluate our loss function, we improve the attention U-Net model by incorporating an image pyramid to preserve contextual features. We experiment on the BUS 2017 dataset and ISIC 2018 dataset where lesions occupy 4.84% and 21.4% of the images area and improve segmentation accuracy when compared to the standard U-Net by 25.7% and 3.6%, respectively.

Key facts. Cited by 172 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Oct 2018.

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Citing papers per year: 2021: 30; 2022: 36; 2023: 32; 2024: 13; 2025: 30; 2026: 31.

172 papers.

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  1. 2022Medical Image Segmentation Review: The success of U-Netcited by 191
  2. 2023CellViT: Vision Transformers for Precise Cell Segmentation and Classificationcited by 108
  3. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  4. 2021Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentationcited by 88
  5. 2022Towards a Guideline for Evaluation Metrics in Medical Image Segmentationcited by 60
  6. 2021NeoUNet: Towards accurate colon polyp segmentation and neoplasm detectioncited by 51
  7. 2023Detection Transformer with Stable Matchingcited by 44
  8. 2022AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Imagescited by 43
  9. 2021Every Annotation Counts: Multi-label Deep Supervision for Medical Image Segmentationcited by 37
  10. 2021Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentationcited by 34
  11. 2023Loss Functions in the Era of Semantic Segmentation: A Survey and Outlookcited by 34
  12. 2022Nuclei instance segmentation and classification in histopathology images with StarDistcited by 31
38 more
  1. 2024Beat this! Accurate beat tracking without DBN postprocessingcited by 27
  2. 2022A Survey on Deep Learning for Skin Lesion Segmentationcited by 22
  3. 2021Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopycited by 20
  4. 2021Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guidingcited by 18
  5. 2021Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentationcited by 17
  6. 2022CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentationcited by 13
  7. 2022Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentationcited by 13
  8. 2023A Generalized Surface Loss for Reducing the Hausdorff Distance in Medical Imaging Segmentationcited by 13
  9. 2021Sharp U-Net: Depthwise Convolutional Network for Biomedical Image Segmentationcited by 12
  10. 2023Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labelscited by 12
  11. 2022BlazeNeo: Blazing fast polyp segmentation and neoplasm detectioncited by 10
  12. 2022MFSNet: A Multi Focus Segmentation Network for Skin Lesion Segmentationcited by 10
  13. 2022FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Braincited by 10
  14. 2021Meningioma segmentation in T1-weighted MRI leveraging global context and attention mechanismscited by 9
  15. 2023Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Cloudscited by 9
  16. 2021High-speed object detection with a single-photon time-of-flight image sensorcited by 7
  17. 2022ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationcited by 7
  18. 2021MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentationcited by 6
  19. 2021Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attentioncited by 6
  20. 2022SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D datacited by 6
  21. 2023Cross Modal Distillation for Flood Extent Mappingcited by 6
  22. 2024MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentationcited by 6
  23. 2024PaveSAM Segment Anything for Pavement Distresscited by 6
  24. 2022Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connectioncited by 5
  25. 2022Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directionscited by 5
  26. 2023Dice Semimetric Losses: Optimizing the Dice Score with Soft Labelscited by 5
  27. 2023Structure Preserving Cycle-GAN for Unsupervised Medical Image Domain Adaptationcited by 5
  28. 2021CovTANet: A Hybrid Tri-level Attention Based Network for Lesion Segmentation, Diagnosis, and Severity Prediction of COVID-19 Chest CT Scanscited by 4
  29. 2021Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imagingcited by 4
  30. 2021Hypernet-Ensemble Learning of Segmentation Probability for Medical Image Segmentation with Ambiguous Labelscited by 4
  31. 2021Distribution-aware Margin Calibration for Semantic Segmentation in Imagescited by 4
  32. 2022Masked Supervised Learning for Semantic Segmentationcited by 4
  33. 2023Cross-dimensional transfer learning in medical image segmentation with deep learningcited by 4
  34. 2023Shape-Sensitive Loss for Catheter and Guidewire Segmentationcited by 4
  35. 2025PARTONOMY: Large Multimodal Models with Part-Level Visual Understandingcited by 4
  36. 2021Classification of Breast Cancer Lesions in Ultrasound Images by using Attention Layer and loss Ensembles in Deep Convolutional Neural Networkscited by 3
  37. 2021SemSegLoss: A python package of loss functions for semantic segmentationcited by 3
  38. 2022Category Guided Attention Network for Brain Tumor Segmentation in MRIcited by 3

50 most cited of 172 All 172 (JSON)