HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT
- URL: http://arxiv.org/abs/2607.26498v2
- Date: Tue, 04 Aug 2026 00:02:12 GMT
- Title: HERMES: A Hybrid Ensemble for Head-and-Neck Tumor Segmentation, TN Staging, and Recurrence-Free Survival on PET/CT
- Abstract summary: HERMES is a single containerized algorithm for the three HECKTOR 2026 subtasks.<n>A 10-fold ensemble of STU-Net Small networks produces the segmentation.<n>HERMES achieved a weighted score of 0.6454 (Mean Dice 0.641, T balanced accuracy 0.580, N balanced accuracy 0.642, RFS C-index 0.679) and qualified for the testing phase.
- Score: 3.2632075797484887
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present HERMES (Hybrid Ensemble for Radiotherapy-target segmentation, Malignancy staging, and Event-free Survival), a single containerized algorithm for the three HECKTOR 2026 subtasks: segmentation of the primary tumor (GTVp) and pathological lymph nodes (GTVn), radiological T/N staging, and recurrence-free survival (RFS), computed from a paired FDG-PET/CT scan and an electronic health record. A 10-fold ensemble of STU-Net Small networks produces the segmentation; the predicted mask then drives two downstream tasks. Rather than pass a generic radiomics vector to the staging models, we derive from the predicted masks a compact set of geometry features aligned with the size and number axes of AJCC/UICC 7th-edition radiological N/T staging. On internal cross-validation these features raise N-stage balanced accuracy from 0.691 to 0.720 (+0.030), our largest single design gain, at lower feature dimensionality. For prognosis we combine complementary deep and clinical risk experts in an equal-weight ensemble, and train one deep expert with a concordance-tracking survival loss of our own, whose value approximates the concordance index during training. Every component was selected on honest out-of-fold predictions under a regularization-oriented protocol, with no tuning on the public validation set, and deployed as two decorrelated submissions. On the HECKTOR 2026 validation leaderboard, HERMES achieved a weighted score of 0.6454 (Mean Dice 0.641, T balanced accuracy 0.580, N balanced accuracy 0.642, RFS C-index 0.679) and qualified for the testing phase. Team: AMC_HNC.
Related papers
- HEad and neCK TumOR (HECKTOR) 2025: Benchmark of Segmentation, Diagnosis, and Prognosis in Multimodal PET/CT [13.031827676281729]
The HECKTOR 2025 challenge established a benchmark for automated HNC analysis using multimodal PET/CT imaging and electronic health records.<n>The challenge attracted 35 registered teams, with 15 final submissions evaluated on a held-out test set.<n>Top-performing algorithms achieved a mean Dice similarity coefficient of 0.75 for segmentation, a concordance index of 0.66 for survival prediction, and a balanced accuracy of 0.56 for HPV classification.
arXiv Detail & Related papers (2026-06-18T12:06:38Z) - FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation [63.7829089874007]
This paper introduces the Fetal Ultrasound Grand Challenge (FUGC), the first benchmark for semi-supervised learning in cervical segmentation.<n>FUGC provides a dataset of 890 TVS images, including 500 training images, 90 validation images, and 300 test images.<n> Methods were evaluated using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and runtime (RT), with a weighted combination of 0.4/0.4/0.2.
arXiv Detail & Related papers (2026-01-22T01:34:39Z) - A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler [49.03919553747297]
We propose an AI-powered, real-time CoW auto-segmentation system capable of efficiently capturing cerebral arteries.<n>No prior studies have explored AI-driven cerebrovascular segmentation using Transcranial Color-coded Doppler (TCCD)<n>The proposed AAW-YOLO demonstrated strong performance in segmenting both ipsilateral and contralateral CoW vessels.
arXiv Detail & Related papers (2025-08-19T14:41:22Z) - Pretrained hybrid transformer for generalizable cardiac substructures segmentation from contrast and non-contrast CTs in lung and breast cancers [3.704003490598663]
AI automated segmentations for radiation treatment planning (RTP) can deteriorate when applied in clinical cases with different characteristics than training dataset.<n>We refined a pretrained transformer into a hybrid transformer convolutional network (HTN) to segment cardiac substructures lung and breast cancer patients.<n>A HTN demonstrated robustly accurate (geometric and dose metrics) cardiac substructures segmentation from CTs with varying imaging and patient characteristics.
arXiv Detail & Related papers (2025-05-16T04:48:33Z) - Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of
Head and Neck Cancers with PET/CT images [6.361835964390572]
3D nnU-Net architecture was adopted to automatic segmentation of primary tumor and lymph nodes synchronously.
Three prognostic models were constructed containing conventional and radiomics features alone.
Dice score and C-index were used as evaluation metrics for segmentation and prognosis task.
arXiv Detail & Related papers (2022-11-18T10:31:26Z) - Recurrence-free Survival Prediction under the Guidance of Automatic
Gross Tumor Volume Segmentation for Head and Neck Cancers [8.598790229614071]
We developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method.
We extracted radiomics features from the segmented tumor volume and constructed a multi-modality tumor recurrence-free survival (RFS) prediction model.
arXiv Detail & Related papers (2022-09-22T18:44:57Z) - Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free
Survival using a Full-Scale UNet with Attention [0.8138288420049126]
The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods.
We trained multiple neural networks for tumor volume segmentation, and these segmentations were ensembled achieving an average Dice Similarity Coefficient of 0.75 in cross-validation.
For prediction of patient progression free survival task, we propose a Cox proportional hazard regression combining clinical, radiomic, and deep learning features.
arXiv Detail & Related papers (2021-11-06T10:28:48Z) - H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd
Place Solution to BraTS Challenge 2020 Segmentation Task [96.49879910148854]
Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions.
We trained and evaluated our model on the Multimodal Brain Tumor Challenge (BraTS) 2020 dataset.
Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.
arXiv Detail & Related papers (2020-12-30T20:44:55Z) - Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net
neural networks: a BraTS 2020 challenge solution [56.17099252139182]
We automate and standardize the task of brain tumor segmentation with U-net like neural networks.
Two independent ensembles of models were trained, and each produced a brain tumor segmentation map.
Our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff 95% of 20.4, 6.7 and 19.5mm on the final test dataset.
arXiv Detail & Related papers (2020-10-30T14:36:10Z) - CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors
and Efficient Neural Networks [51.589769497681175]
The novel coronavirus (SARS-CoV-2) has led to a pandemic.
The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands.
We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
arXiv Detail & Related papers (2020-07-20T21:47:28Z) - Automated Quantification of CT Patterns Associated with COVID-19 from
Chest CT [48.785596536318884]
The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions.
The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities.
Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
arXiv Detail & Related papers (2020-04-02T21:49:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.