Ensemble YOLO Framework for Multi-Domain Mitotic Figure Detection in Histopathology Images

• URL: http://arxiv.org/abs/2509.02957v2
• Date: Sat, 20 Sep 2025 08:43:08 GMT
• Title: Ensemble YOLO Framework for Multi-Domain Mitotic Figure Detection in Histopathology Images
• Authors: Navya Sri Kelam, Akash Parekh, Saikiran Bonthu, Nitin Singhal,
• Abstract summary: Two modern one-stage detectors, YOLOv5 and YOLOv8, were trained on MIDOG++, CMC, and CCMCT datasets.<n>YOLOv5 achieved higher precision (84.3%), while YOLOv8 offered improved recall (82.6%)<n>Our ensemble ranked 5th with an F1 score of 79.2%, precision of 73.6%, and recall of 85.8%, confirming that the proposed strategy generalizes effectively across unseen test data.
• Score: 0.7541656202645494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The reliable identification of mitotic figures in whole-slide histopathological images remains difficult, owing to their low prevalence, substantial morphological heterogeneity, and the inconsistencies introduced by tissue processing and staining procedures. The MIDOG competition series provides standardized benchmarks for evaluating detection approaches across diverse domains, thus motivating the development of generalizable deep learning models. In this work, we investigate the performance of two modern one-stage detectors, YOLOv5 and YOLOv8, trained on MIDOG++, CMC, and CCMCT datasets. To enhance robustness, training incorporated stain-invariant color perturbations and texture-preserving augmentations. Ininternal validation, YOLOv5 achieved higher precision (84.3%), while YOLOv8 offered improved recall (82.6%), reflecting architectural trade-offs between anchor-based and anchor-free detections. To capitalize on their complementary strengths, weemployed an ensemble of the two models, which improved sensitivity (85.3%) while maintaining competitive precision, yielding the best F1 score of 83.1%. On the preliminary MIDOG 2025 test leaderboard, our ensemble ranked 5th with an F1 score of 79.2%, precision of 73.6%, and recall of 85.8%, confirming that the proposed strategy generalizes effectively across unseen test data. These findings highlight the effectiveness of combining anchor-based and anchor-free object detectors to advance automated mitosis detection in digital pathology.

Related papers

• DAONet-YOLOv8: An Occlusion-Aware Dual-Attention Network for Tea Leaf Pest and Disease Detection [2.661320179262946]
  We propose an enhanced YOLOv8 variant with three key improvements to accurately detect tea leaf pests and diseases.<n>ExistingNet-YOLOv8 achieves 92.97% precision, 92.80% recall, 97.10% mAP@50 and 76.90% mAP@50:95, outperforming the YOLOv8n baseline by 2.34, 4.68, 1.40 and 1.80 percentage points respectively.
  arXiv  Detail & Related papers  (2025-11-28T14:28:30Z)
• CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design [46.12506067241116]
  We present CODE (Chain of Diffusion Embeddings), a self evaluating metric to quantify topological frustration.<n>We propose CONFIDE, a unified evaluation framework that combines energetic and topological perspectives.<n>By combining data driven embeddings with theoretical insight, CODE and CONFIDE outperform existing metrics across a wide range of biomolecular systems.
  arXiv  Detail & Related papers  (2025-11-20T03:38:46Z)
• Robust Atypical Mitosis Classification with DenseNet121: Stain-Aware Augmentation and Hybrid Loss for Domain Generalization [16.048949963804123]
  We present a DenseNet-121-based framework tailored for atypical mitosis classification in the MIDOG 2025 (Track 2) setting.<n>Our method integrates stain-aware augmentation (Macenko), geometric and intensity transformations, and imbalance-aware learning via weighted sampling with a hybrid objective combining class-weighted binary cross-entropy and focal loss.<n> Trained end-to-end with AdamW and evaluated across multiple independent domains, the model demonstrates strong generalization under scanner and staining shifts, achieving balanced accuracy 85.0%, AUROC 0.927, sensitivity 89.2%, and specificity 80.9% on the official test set.
  arXiv  Detail & Related papers  (2025-10-26T11:24:55Z)
• Parameter-efficient fine-tuning (PEFT) of Vision Foundation Models for Atypical Mitotic Figure Classification [0.0]
  Atypical mitotic figures (AMFs) are rare abnormal cell divisions associated with tumor aggressiveness and poor prognosis.<n>The MID 2025OG challenge introduced a dedicated track for atypical mitosis classification.<n>We investigated the use of large vision foundation models, including Virchow, Virchow2, and UNI, with Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning.
  arXiv  Detail & Related papers  (2025-09-21T05:46:54Z)
• A Single Detect Focused YOLO Framework for Robust Mitotic Figure Detection [0.0]
  We introduce SDF-YOLO, a lightweight yet domain-robust detection framework for small, rare targets such as mitotic figures.<n>The model builds on YOLOv11 with task-specific modifications, including a single detection head aligned with mitotic figure scale.<n>It achieved an average precision (AP) of 0.799, with a precision of 0.758, a recall of 0.775, an F1 score of 0.766, and an FROC-AUC of 5.793.
  arXiv  Detail & Related papers  (2025-09-01T20:41:48Z)
• A bag of tricks for real-time Mitotic Figure detection [0.0]
  We build on the efficient RTMDet single stage object detector to achieve high inference speed suitable for clinical deployment.<n>We employ targeted, hard negative mining on necrotic and debris tissue to reduce false positives.<n>On the preliminary test set of the MItosis DOmain Generalization (MIDOG) 2025 challenge, our single-stage RTMDet-S based approach reaches an F1 of 0.81.
  arXiv  Detail & Related papers  (2025-08-27T11:45:44Z)
• YOLO-RS: Remote Sensing Enhanced Crop Detection Methods [0.32985979395737786]
  Existing target detection methods show poor performance when dealing with small targets in remote sensing images.<n>YOLO-RS is based on the latest Yolov11 which significantly enhances the detection of small targets.<n>Experiments validate the effectiveness and application potential of YOLO-RS in the task of detecting small targets in remote sensing images.
  arXiv  Detail & Related papers  (2025-04-15T13:13:22Z)
• Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing [10.564949684320727]
  SegRMT is a testing approach that leverages genetic algorithms to optimize sequences of spatial and spectral transformations.<n>Our experiments show that SegRMT reduces DeepLabV3's mean Intersection over Union (mIoU) to 6.4%.<n>When used for adversarial training, SegRMT boosts model performance, achieving mIoU improvements up to 73%.
  arXiv  Detail & Related papers  (2025-04-03T07:15:45Z)
• Urinary Tract Infection Detection in Digital Remote Monitoring: Strategies for Managing Participant-Specific Prediction Complexity [43.108040967674185]
  Urinary tract infections (UTIs) are a significant health concern, particularly for people living with dementia (PLWD)<n>This study builds on previous work that utilised machine learning (ML) to detect UTIs in PLWD.
  arXiv  Detail & Related papers  (2025-02-18T12:01:55Z)
• Enhancing Reconstruction-Based Out-of-Distribution Detection in Brain MRI with Model and Metric Ensembles [2.6123133623254193]
  Out-of-distribution (OOD) detection is crucial for safely deploying automated medical image analysis systems.<n>We investigated the effectiveness of a reconstruction-based autoencoder for unsupervised detection of synthetic artifacts in brain MRI.
  arXiv  Detail & Related papers  (2024-12-23T13:58:52Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• CRTRE: Causal Rule Generation with Target Trial Emulation Framework [47.2836994469923]
  We introduce a novel method called causal rule generation with target trial emulation framework (CRTRE) CRTRE applies randomize trial design principles to estimate the causal effect of association rules. We then incorporate such association rules for the downstream applications such as prediction of disease onsets.
  arXiv  Detail & Related papers  (2024-11-10T02:40:06Z)
• PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
  We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation. Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process. Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
  arXiv  Detail & Related papers  (2024-09-08T15:02:25Z)
• StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model [62.25424831998405]
  StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples. It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
  arXiv  Detail & Related papers  (2024-08-11T01:22:29Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology [1.488519799639108]
  Machine learning algorithms have the potential to improve patient outcomes in digital pathology. generalization is limited by sensitivity to variations in tissue preparation, staining procedures and scanning equipment. We studied the effectiveness of two Synthetic DOmain-Targeted Augmentation (S-DOTA) methods, namely CycleGAN-enabled Scanner Transform (ST) and targeted Stain Vector Augmentation (SVA) We evaluated the ability of these techniques to improve model generalization to various tasks and settings.
  arXiv  Detail & Related papers  (2023-05-03T19:53:30Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation [49.421099172544196]
  We propose a novel semantic-guided feature imitation technique, which automatically performs soft matching between feature pairs across all pyramid levels. We also introduce contrastive distillation to effectively capture the information encoded in the relationship between different feature regions. Our method consistently outperforms the existing detection KD techniques, and works when (1) components in the framework are used separately and in conjunction.
  arXiv  Detail & Related papers  (2021-08-17T07:44:27Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

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.