ArticleScientific reports2021
A multi-phase deep CNN based mitosis detection framework for breast cancer histopathological images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- Integration of Histologic Pattern Classifiers and Graph Convolutional Networks for Prognostic Prediction in Lung Adenocarcinoma.JTO clinical and research reports · 2026Article
- Deep learning model for detecting high-grade dysplasia in colorectal adenomas.Journal of pathology informatics · 2025Article
- Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms.PloS one · 2025Article
- A deep learning framework deploying segment anything to detect pan-cancer mitotic figures from haematoxylin and eosin-stained slides.Communications biology · 2024Article
- Scalable deep learning artificial intelligence histopathology slide analysis and validation.Scientific reports · 2024Article
- [Research progress of breast pathology image diagnosis based on deep learning].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Review
- Enhancing mitosis quantification and detection in meningiomas with computational digital pathology.Acta neuropathologica communications · 2024Article
- Low-Cost Histopathological Mitosis Detection for Microscope-acquired Images.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs.Expert systems with applications · 2023Article
- Mitotic Nuclei Segmentation and Classification Using Chaotic Butterfly Optimization Algorithm with Deep Learning on Histopathology Images.Biomimetics (Basel, Switzerland) · 2023Article
- Breast Cancer Histopathological Images Segmentation Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- A Survey of Convolutional Neural Network in Breast Cancer.Computer modeling in engineering & sciences : CMES · 2023Article
- A survey on recent trends in deep learning for nucleus segmentation from histopathology images.Evolving systems · 2023Review
- Computer-aided detection and prognosis of colorectal cancer on whole slide images using dual resolution deep learning.Journal of cancer research and clinical oncology · 2023Article
- VSGD-Net: Virtual Staining Guided Melanocyte Detection on Histopathological Images.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2023Article
- Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer.PloS one · 2023Article
- Ransomware detection using deep learning based unsupervised feature extraction and a cost sensitive Pareto Ensemble classifier.Scientific reports · 2022Article
- Chaotic Sparrow Search Algorithm with Deep Transfer Learning Enabled Breast Cancer Classification on Histopathological Images.Cancers · 2022Article
- COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN.Diagnostics (Basel, Switzerland) · 2022Article
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5 authors.
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Abstract
The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework "MP-MitDet" for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier "MitosRes-CNN" to filter false mitoses. The performance of the proposed "MitosRes-CNN" is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.
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