A bag of tricks for real-time Mitotic Figure detection

• URL: http://arxiv.org/abs/2508.19804v1
• Date: Wed, 27 Aug 2025 11:45:44 GMT
• Title: A bag of tricks for real-time Mitotic Figure detection
• Authors: Christian Marzahl, Brian Napora,
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mitotic figure (MF) detection in histopathology images is challenging due to large variations in slide scanners, staining protocols, tissue types, and the presence of artifacts. This paper presents a collection of training techniques - a bag of tricks - that enable robust, real-time MF detection across diverse domains. We build on the efficient RTMDet single stage object detector to achieve high inference speed suitable for clinical deployment. Our method addresses scanner variability and tumor heterogeneity via extensive multi-domain training data, balanced sampling, and careful augmentation. Additionally, we employ targeted, hard negative mining on necrotic and debris tissue to reduce false positives. In a grouped 5-fold cross-validation across multiple MF datasets, our model achieves an F1 score between 0.78 and 0.84. 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, outperforming larger models and demonstrating adaptability to new, unfamiliar domains. The proposed solution offers a practical trade-off between accuracy and speed, making it attractive for real-world clinical adoption.

Related papers

• Deep Unsupervised Anomaly Detection in Brain Imaging: Large-Scale Benchmarking and Bias Analysis [42.60508892284938]
  We present a large-scale, multi-center benchmark of deep unsupervised anomaly detection for brain imaging.<n>We tested 2,221 T1w and 1,262 T2w scans spanning healthy datasets and diverse clinical cohorts.<n>Our benchmark establishes a transparent foundation for future research and highlights priorities for clinical translation.
  arXiv  Detail & Related papers  (2025-12-01T11:03:27Z)
• Challenges and Lessons from MIDOG 2025: A Two-Stage Approach to Domain-Robust Mitotic Figure Detection [9.314972045525133]
  This paper describes our participation in the MIDOG 2025 challenge, focusing on robust mitotic figure detection.<n>We developed a two-stage pipeline combining Faster R-CNN for candidate detection with an ensemble of three classifiers for false positive reduction.<n>Our best submission achieved F1-score 0.2237 (Recall: 0.9528, Precision: 0.1267) using a Faster R-CNN trained solely on MIDOG++ dataset.
  arXiv  Detail & Related papers  (2025-09-01T17:42:05Z)
• RF-DETR for Robust Mitotic Figure Detection: A MIDOG 2025 Track 1 Approach [0.0]
  This paper presents our approach for the MIDOG 2025 challenge Track 1, focusing on robust mitotic figure detection across diverse histological contexts.<n>We employed RF-DETR (Roboflow Detection Transformer) with hard negative mining, trained on MIDOG++ dataset.<n>On the preliminary test set, our method achieved an F1 score of 0.789 with a recall of 0.839 and precision of 0.746, demonstrating effective generalization across unseen domains.
  arXiv  Detail & Related papers  (2025-08-29T16:04:50Z)
• From Promise to Practical Reality: Transforming Diffusion MRI Analysis with Fast Deep Learning Enhancement [55.64033992736822]
  FastFOD-Net is an end-to-end deep learning framework enhancing FODs with superior performance and delivering training/inference efficiency for clinical use.<n>This work will facilitate the more widespread adoption of, and build clinical trust in, deep learning based methods for diffusion MRI enhancement.
  arXiv  Detail & Related papers  (2025-08-13T17:56:29Z)
• A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusion [5.15423063632115]
  Early and accurate interpretation of screening mammograms is essential for effective breast cancer detection.<n>Existing AI approaches fall short by focusing on single view inputs or single-task outputs.<n>We propose a novel multi-view, multitask hybrid deep learning framework that processes all four standard mammography views.
  arXiv  Detail & Related papers  (2025-07-22T18:52:18Z)
• Lightweight Relational Embedding in Task-Interpolated Few-Shot Networks for Enhanced Gastrointestinal Disease Classification [0.0]
  Colon cancer detection is crucial for increasing patient survival rates.<n> colonoscopy is dependent on obtaining adequate and high-quality endoscopic images.<n>Few-Shot Learning architecture enables our model to rapidly adapt to unseen fine-grained endoscopic image patterns.<n>Our model demonstrated superior performance, achieving an accuracy of 90.1%, precision of 0.845, recall of 0.942, and an F1 score of 0.891.
  arXiv  Detail & Related papers  (2025-05-30T16:54:51Z)
• ADformer: A Multi-Granularity Spatial-Temporal Transformer for EEG-Based Alzheimer Detection [42.72554952799386]
  EEG has emerged as a cost-effective and efficient tool to support neurologists in the detection of Alzheimer's Disease (AD)<n>We propose ADformer, a novel multi-granularity spatial-temporal transformer designed to capture both temporal and spatial features from raw EEG signals.<n> Experimental results demonstrate that ADformer consistently outperforms existing methods, achieving subject-level F1 scores of 92.82%, 89.83%, 67.99%, and 83.98% on the 4 datasets, respectively.
  arXiv  Detail & Related papers  (2024-08-17T14:10:41Z)
• Affine-Consistent Transformer for Multi-Class Cell Nuclei Detection [76.11864242047074]
  We propose a novel Affine-Consistent Transformer (AC-Former), which directly yields a sequence of nucleus positions. We introduce an Adaptive Affine Transformer (AAT) module, which can automatically learn the key spatial transformations to warp original images for local network training. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art algorithms on various benchmarks.
  arXiv  Detail & Related papers  (2023-10-22T02:27:02Z)
• Mitosis Detection, Fast and Slow: Robust and Efficient Detection of Mitotic Figures [3.047950378303433]
  We propose a robust and efficient two-stage mitosis detection framework, which comprises mitosis candidate segmentation and candidate refinement stages. EUNet can precisely segment candidates at a lower resolution to considerably speed up candidate detection. We demonstrate state-of-the-art performance and generalizability of the proposed model on the three largest publicly available mitosis datasets.
  arXiv  Detail & Related papers  (2022-08-26T11:14:59Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• Stain-Robust Mitotic Figure Detection for the Mitosis Domain Generalization Challenge [2.072197863131669]
  The MItosis DOmain Generalization (MIDOG) challenge aims to test the robustness of detection models on unseen data from multiple scanners. We present a short summary of the approach employed by the TIA Centre team to address this challenge.
  arXiv  Detail & Related papers  (2021-09-02T11:44:42Z)
• Quantifying the Scanner-Induced Domain Gap in Mitosis Detection [8.09551131543818]
  We evaluate the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner. Our work indicates that the domain shift induced not by biochemical variability but purely by the choice of acquisition device is underestimated.
  arXiv  Detail & Related papers  (2021-03-30T17:17:25Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Grasps [46.316638161863025]
  We present Multi-FinGAN, a fast generative multi-finger grasp sampling method that synthesizes high quality grasps directly from RGB-D images in about a second. We experimentally validate and benchmark our method against a standard grasp-sampling method on 790 grasps in simulation and 20 grasps on a real Franka Emika Panda. Remarkably, our approach is up to 20-30 times faster than the baseline, a significant improvement that opens the door to feedback-based grasp re-planning and task informative grasping.
  arXiv  Detail & Related papers  (2020-12-17T16:08:18Z)

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.