Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025

• URL: http://arxiv.org/abs/2509.02597v1
• Date: Fri, 29 Aug 2025 15:16:35 GMT
• Title: Solutions for Mitotic Figure Detection and Atypical Classification in MIDOG 2025
• Authors: Shuting Xu, Runtong Liu, Zhixuan Chen, Junlin Hou, Hao Chen,
• Abstract summary: We present our approach to the Mitosis Domain Generalization (MIDOG) 2025 Challenge, which consists of two distinct tasks.<n>For the mitotic figure detection task, we propose a two-stage detection-classification framework that first localizes candidate mitotic figures.<n>For the atypical mitosis classification task, we employ an ensemble strategy that integrates predictions from multiple state-of-the-art deep learning architectures.
• Score: 9.85180336249631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has driven significant advances in mitotic figure analysis within computational pathology. In this paper, we present our approach to the Mitosis Domain Generalization (MIDOG) 2025 Challenge, which consists of two distinct tasks, i.e., mitotic figure detection and atypical mitosis classification. For the mitotic figure detection task, we propose a two-stage detection-classification framework that first localizes candidate mitotic figures and subsequently refines the predictions using a dedicated classification module. For the atypical mitosis classification task, we employ an ensemble strategy that integrates predictions from multiple state-of-the-art deep learning architectures to improve robustness and accuracy. Extensive experiments demonstrate the effectiveness of our proposed methods across both tasks.

Related papers

• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Teacher-Student Model for Detecting and Classifying Mitosis in the MIDOG 2025 Challenge [0.5794811300616634]
  Counting mitotic figures is time-intensive for pathologists and leads to inter-observer variability.<n>Artificial intelligence (AI) promises a solution by automatically detecting mitotic figures while maintaining decision consistency.<n>We formulate mitosis detection as a pixel-level segmentation and propose a teacher-student model that simultaneously addresses mitosis detection and classification.
  arXiv  Detail & Related papers  (2025-09-03T18:08:11Z)
• 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)
• Foundation Model-Driven Classification of Atypical Mitotic Figures with Domain-Aware Training Strategies [0.0]
  We present a solution for the MIDOG 2025 Challenge Track2, addressing binary classification of normal mitotic figures (NMFs) versus atypical mitotic figures (AMFs)
  arXiv  Detail & Related papers  (2025-08-29T17:38:33Z)
• A multi-task neural network for atypical mitosis recognition under domain shift [3.2490463485798338]
  An approach based on multi-task learning is proposed for addressing this problem.<n>By exploiting auxiliary tasks, correlated to the main classification task, the proposed approach aims to aid the model to focus only on the object to classify.<n>The proposed approach shows promising performance in a preliminary evaluation conducted on three distinct datasets.
  arXiv  Detail & Related papers  (2025-08-28T17:39:30Z)
• Automated Classification of Normal and Atypical Mitotic Figures Using ConvNeXt V2: MIDOG 2025 Track 2 [0.026042848991788176]
  This paper presents our solution for the MIDOG 2025 Challenge Track 2, which focuses on binary classification of normal mitotic figures (NMFs) versus atypical mitotic figures (AMFs)<n>Our approach leverages a ConvNeXt V2 base model with center cropping preprocessing and 5-fold cross-validation ensemble strategy.<n>The solution demonstrates the effectiveness of modern convolutional architectures for mitotic figure subtyping while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2025-08-26T09:11:12Z)
• Anomaly Detection by Adapting a pre-trained Vision Language Model [48.225404732089515]
  We present a unified framework named CLIP-ADA for Anomaly Detection by Adapting a pre-trained CLIP model. We introduce the learnable prompt and propose to associate it with abnormal patterns through self-supervised learning. We achieve the state-of-the-art 97.5/55.6 and 89.3/33.1 on MVTec-AD and VisA for anomaly detection and localization.
  arXiv  Detail & Related papers  (2024-03-14T15:35:07Z)
• Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence [56.092059713922744]
  We show that using high-level contextualized features as prediction targets can achieve superior performance. Specifically, we propose Skeleton2vec, a simple and efficient self-supervised 3D action representation learning framework. Our proposed Skeleton2vec outperforms previous methods and achieves state-of-the-art results.
  arXiv  Detail & Related papers  (2024-01-01T12:08:35Z)
• A kinetic approach to consensus-based segmentation of biomedical images [39.58317527488534]
  We apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. The large time behavior of the system is then computed with the aid of a surrogate Fokker-Planck approach. We minimize the introduced segmentation metric for a relevant set of 2D gray-scale images.
  arXiv  Detail & Related papers  (2022-11-08T09:54:34Z)
• 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)
• Learning Discriminative Representation via Metric Learning for Imbalanced Medical Image Classification [52.94051907952536]
  We propose embedding metric learning into the first stage of the two-stage framework specially to help the feature extractor learn to extract more discriminative feature representations. Experiments mainly on three medical image datasets show that the proposed approach consistently outperforms existing onestage and two-stage approaches.
  arXiv  Detail & Related papers  (2022-07-14T14:57:01Z)
• Distribution Alignment: A Unified Framework for Long-tail Visual Recognition [52.36728157779307]
  We propose a unified distribution alignment strategy for long-tail visual recognition. We then introduce a generalized re-weight method in the two-stage learning to balance the class prior. Our approach achieves the state-of-the-art results across all four recognition tasks with a simple and unified framework.
  arXiv  Detail & Related papers  (2021-03-30T14:09:53Z)
• 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.