ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2022
Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
140 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning algorithm performance in contouring head and neck organs at risk: a systematic review and single-arm meta-analysis.Biomedical engineering online · 2023Pooled it
- Segmenting scaffolds with ingrown bone in micro-CT images: considerations of evaluation metrics and loss functions.Medical & biological engineering & computing · 2026Article
- DentSeg3: A Three-Branch Specialized Fusion Network for Dental Lesion Segmentation in Color Images.Journal of imaging informatics in medicine · 2026Article
- AFFUNet: adaptive feature fusion Transformer U-Net with joint loss function for medical image segmentation.Quantitative imaging in medicine and surgery · 2026Article
- Toward AI-Assisted Precision Diagnostics in Breast Cancer: Source-Group-Preserving Evaluation of Class-Imbalance Strategies for Ordered ER-IHC Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- High-resolution mapping reveals a profound gap between existing rooftop solar deployment and potential in Africa.iScience · 2026Article
- Deep Learning-Assisted Quality Control of Histology Teaching Slides: Detection and Localization of Tissue Fold Artifacts in H&E-Stained Images.Bioengineering (Basel, Switzerland) · 2026Article
- Human-AI Interaction With AI-Assisted Tumor Overlays in Pediatric Whole-Body Magnetic Resonance Imaging: Exploratory Reader Study.JMIR human factors · 2026Article
- Development of an Artificial Intelligence-Driven Three-Dimensional Reconstruction Model of Renal Vasculature for Surgical Planning in Robot-Assisted Partial Nephrectomy.The international journal of medical robotics + computer assisted surgery : MRCAS · 2026Article
- Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach.Medical & biological engineering & computing · 2026Article
- A multimodal complementary feature fusion network for myocardial pathological segmentation in multi-sequence cardiac magnetic resonance images.Quantitative imaging in medicine and surgery · 2026Article
- Quantifying cortical lesions in multiple sclerosis MRI datasets using multi-contrast post-processing and deep learning.Communications medicine · 2026Article
- Computed tomography-based deep learning prediction of radiation-induced functional decline after lung cancer radiotherapy.Translational lung cancer research · 2026Article
- Artificial Intelligence-Assisted Inner Ear Computed Tomography Analysis: Radiomics-Based Comparison of Affected and Unaffected Ears in Idiopathic Sudden Sensorineural Hearing Loss.Journal of imaging informatics in medicine · 2026Article
- Deep learning for time-series segmentation of mechanical ventilator waveforms.Scientific reports · 2026Article
- [Research on surgical instrument segmentation algorithm based on frequency-domain adaptive feature decomposition and visual Mamba].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- A two-stage deep-learning model using MobileNetV2 and U-Net for CT-based muscle volume assessment in total hip arthroplasty.Scientific reports · 2026Article
- Learning continuous activation fields from microscopic SEM images of lycoperdioid fungi via CNN-guided neural operator modeling.Scientific reports · 2026Article
- Deep learning-based neuroanatomical profiling reveals population-specific brain changes in multiple sclerosis: a large-scale Middle Eastern study.BMC medical imaging · 2026Observational
80 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Automatic segmentation methods are an important advancement in medical image analysis. Machine learning techniques, and deep neural networks in particular, are the state-of-the-art for most medical image segmentation tasks. Issues with class imbalance pose a significant challenge in medical datasets, with lesions often occupying a considerably smaller volume relative to the background. Loss functions used in the training of deep learning algorithms differ in their robustness to class imbalance, with direct consequences for model convergence. The most commonly used loss functions for segmentation are based on either the cross entropy loss, Dice loss or a combination of the two. We propose the Unified Focal loss, a new hierarchical framework that generalises Dice and cross entropy-based losses for handling class imbalance. We evaluate our proposed loss function on five publicly available, class imbalanced medical imaging datasets: CVC-ClinicDB, Digital Retinal Images for Vessel Extraction (DRIVE), Breast Ultrasound 2017 (BUS2017), Brain Tumour Segmentation 2020 (BraTS20) and Kidney Tumour Segmentation 2019 (KiTS19). 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 to class imbalance and consistently outperforms the other loss functions. Source code is available at: https://github.com/mlyg/unified-focal-loss.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.