Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector

• URL: http://arxiv.org/abs/2212.05900v1
• Date: Mon, 12 Dec 2022 13:57:38 GMT
• Title: Deep learning-based Subtyping of Atypical and Normal Mitoses using a Hierarchical Anchor-Free Object Detector
• Authors: Marc Aubreville, Jonathan Ganz, Jonas Ammeling, Taryn A. Donovan, Rutger H. J. Fick, Katharina Breininger, Christof A. Bertram
• Abstract summary: Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories. We set up a state-of-the-art object detection pipeline extending the anchor-free FCOS approach with a gated hierarchical subclassification branch.
• Score: 0.802219018904343
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mitotic activity is key for the assessment of malignancy in many tumors. Moreover, it has been demonstrated that the proportion of abnormal mitosis to normal mitosis is of prognostic significance. Atypical mitotic figures (MF) can be identified morphologically as having segregation abnormalities of the chromatids. In this work, we perform, for the first time, automatic subtyping of mitotic figures into normal and atypical categories according to characteristic morphological appearances of the different phases of mitosis. Using the publicly available MIDOG21 and TUPAC16 breast cancer mitosis datasets, two experts blindly subtyped mitotic figures into five morphological categories. Further, we set up a state-of-the-art object detection pipeline extending the anchor-free FCOS approach with a gated hierarchical subclassification branch. Our labeling experiment indicated that subtyping of mitotic figures is a challenging task and prone to inter-rater disagreement, which we found in 24.89% of MF. Using the more diverse MIDOG21 dataset for training and TUPAC16 for testing, we reached a mean overall average precision score of 0.552, a ROC AUC score of 0.833 for atypical/normal MF and a mean class-averaged ROC-AUC score of 0.977 for discriminating the different phases of cells undergoing mitosis.

Related papers

• 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)
• Normal and Atypical Mitosis Image Classifier using Efficient Vision Transformer [0.5536078880492113]
  We tackle atypical versus normal mitosis classification in the MIDOG 2025 challenge using EfficientViT-L2.<n>A unified dataset of 13,938 nuclei from seven cancer types was used, with atypical mitoses comprising 15.<n>This model achieved balanced accuracy of 0.859, ROC AUC of 0.942, and raw accuracy of 0.85, demonstrating competitive and well-balanced performance across metrics.
  arXiv  Detail & Related papers  (2025-08-28T23:45:06Z)
• Mix, Align, Distil: Reliable Cross-Domain Atypical Mitosis Classification [5.484561603970499]
  We present a simple training-time recipe for domain-robust AMF classification in MIDOG 2025 Task 2.<n>Our submission attains balanced accuracy of 0.8762, sensitivity of 0.8873, specificity of 0.8651, and ROC AUC of 0.9499.
  arXiv  Detail & Related papers  (2025-08-28T13:04:55Z)
• Benchmarking Deep Learning and Vision Foundation Models for Atypical vs. Normal Mitosis Classification with Cross-Dataset Evaluation [0.31571255367331746]
  We build on the Atypical Mitosis dataset for Breast Cancer (AMi-Br)<n>This study presents a benchmark comparing deep learning approaches for automated atypical mitotic figure (AMF) classification.<n>We find average balanced accuracy values of up to 0.8135, 0.7788, and 0.7723 on the in-domain AMi-Br and the out-of-domain AtNorm-Br and AtNorM-MD datasets, respectively.
  arXiv  Detail & Related papers  (2025-06-26T16:30:42Z)
• Graph Classification and Radiomics Signature for Identification of Tuberculous Meningitis [2.2301876577897968]
  Tuberculous meningitis (TBM) is a serious brain infection caused by Mycobacterium tuberculosis. This study aims to classify TBM patients using T1-weighted (T1w) non-contrast Magnetic Resonance Imaging (MRI) scans.
  arXiv  Detail & Related papers  (2025-04-01T16:28:39Z)
• Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br) [0.2786153781225932]
  Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology might be an independent prognostic criterion for breast cancer. We present the first ever publicly available dataset of atypical and normal MFs (AMi-Br)
  arXiv  Detail & Related papers  (2025-01-08T12:41:42Z)
• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.76736949127792]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge.<n>The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas.<n>The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI [2.7962860265843563]
  We present a framework for automated fetal vessel segmentation from 3D black blood T2w MRI and anomaly classification. We target 11 cardiac vessels and three distinct aortic arch anomalies, including double aortic arch, right aortic arch, and suspected coarctation of the aorta. Our results showcase that our proposed training strategy significantly outperforms label propagation and a network trained exclusively on propagated labels.
  arXiv  Detail & Related papers  (2023-11-13T10:54:53Z)
• Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography [4.123006816939975]
  Deep learning models for abnormality classification can perform well in screening mammography. The demographic, imaging, and clinical characteristics associated with increased risk of model failure remain unclear. We assessed model performance by subgroups defined by age, race, pathologic outcome, tissue density, and imaging characteristics.
  arXiv  Detail & Related papers  (2023-05-08T02:28:45Z)
• MesoGraph: Automatic Profiling of Malignant Mesothelioma Subtypes from Histological Images [0.0]
  We develop a novel dual-task Graph Neural Network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score of all the cells in the sample. We validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score, finding that some of the morphological differences identified by our model match known differences used by pathologists.
  arXiv  Detail & Related papers  (2023-02-23T11:11:55Z)
• Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps [48.498376125522114]
  This paper proposes a UNet-based segmentation approach that requires minimal post-processing. It achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy.
  arXiv  Detail & Related papers  (2022-10-13T15:34:20Z)
• HSADML: Hyper-Sphere Angular Deep Metric based Learning for Brain Tumor Classification [3.319978067919918]
  HSADML is a novel framework which enables deep metric learning (DML) using SphereFace Loss. State-of-the-art 98.69% validation accu-racy using k-NN (k=1)
  arXiv  Detail & Related papers  (2022-01-28T17:37:15Z)
• Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types [60.45942774425782]
  We introduce anomaly clustering, whose goal is to group data into coherent clusters of anomaly types. This is different from anomaly detection, whose goal is to divide anomalies from normal data. We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods.
  arXiv  Detail & Related papers  (2021-12-21T23:11:33Z)
• Multi-Scale Input Strategies for Medulloblastoma Tumor Classification using Deep Transfer Learning [59.30734371401316]
  Medulloblastoma is the most common malignant brain cancer among children. CNN has shown promising results for MB subtype classification. We study the impact of tile size and input strategy.
  arXiv  Detail & Related papers  (2021-09-14T09:42:37Z)
• 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)
• Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation [48.504790189796836]
  We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe) We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling. CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
  arXiv  Detail & Related papers  (2020-05-27T06:58:39Z)

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.