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Tang Q, Cai Y. Deep radial basis function networks with subcategorization for mitosis detection in breast histopathology images. Med Image Anal 2024; 95:103204. [PMID: 38761438 DOI: 10.1016/j.media.2024.103204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 04/10/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
Due to the intra-class diversity of mitotic cells and the morphological overlap with similarly looking imposters, automatic mitosis detection in histopathology slides is still a challenging task. In this paper, we propose a novel mitosis detection model in a weakly supervised way, which consists of a candidate proposal network and a verification network. The candidate proposal network based on patch learning aims to separate both mitotic cells and their mimics from the background as candidate objects, which substantially reduces missed detections in the screening process of candidates. These obtained candidate results are then fed into the verification network for mitosis refinement. The verification network adopts an RBF-based subcategorization scheme to deal with the problems of high intra-class variability of mitosis and the mimics with similar appearance. We utilize the RBF centers to define subcategories containing mitotic cells with similar properties and capture representative RBF center locations through joint training of classification and clustering. Due to the lower intra-class variation within a subcategory, the localized feature space at subcategory level can better characterize a certain type of mitotic figures and can provide a better similarity measurement for distinguishing mitotic cells from nonmitotic cells. Our experiments manifest that this subcategorization scheme helps improve the performance of mitosis detection and achieves state-of-the-art results on the publicly available mitosis datasets using only weak labels.
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Affiliation(s)
- Qiling Tang
- School of Biomedical Engineering, South Central Minzu University, Wuhan 430074, PR China.
| | - Yu Cai
- School of Biomedical Engineering, South Central Minzu University, Wuhan 430074, PR China.
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Rai T, Morisi A, Bacci B, Bacon NJ, Dark MJ, Aboellail T, Thomas SA, La Ragione RM, Wells K. Keeping Pathologists in the Loop and an Adaptive F1-Score Threshold Method for Mitosis Detection in Canine Perivascular Wall Tumours. Cancers (Basel) 2024; 16:644. [PMID: 38339394 PMCID: PMC10854568 DOI: 10.3390/cancers16030644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/17/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Performing a mitosis count (MC) is the diagnostic task of histologically grading canine Soft Tissue Sarcoma (cSTS). However, mitosis count is subject to inter- and intra-observer variability. Deep learning models can offer a standardisation in the process of MC used to histologically grade canine Soft Tissue Sarcomas. Subsequently, the focus of this study was mitosis detection in canine Perivascular Wall Tumours (cPWTs). Generating mitosis annotations is a long and arduous process open to inter-observer variability. Therefore, by keeping pathologists in the loop, a two-step annotation process was performed where a pre-trained Faster R-CNN model was trained on initial annotations provided by veterinary pathologists. The pathologists reviewed the output false positive mitosis candidates and determined whether these were overlooked candidates, thus updating the dataset. Faster R-CNN was then trained on this updated dataset. An optimal decision threshold was applied to maximise the F1-score predetermined using the validation set and produced our best F1-score of 0.75, which is competitive with the state of the art in the canine mitosis domain.
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Affiliation(s)
- Taranpreet Rai
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK;
- Surrey DataHub, University of Surrey, Guildford GU2 7AL, UK
| | - Ambra Morisi
- School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK; (A.M.); (R.M.L.R.)
| | - Barbara Bacci
- Department of Veterinary Medical Sciences, University of Bologna, 40126 Bologna, Italy;
| | | | - Michael J. Dark
- Department of Comparative, Diagnostic and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, FL 32611, USA;
| | - Tawfik Aboellail
- Department of Diagnostic Pathology and Pathobiology, Kansas State University, Manhattan, KS 66506, USA;
| | - Spencer A. Thomas
- Department of Computer Science, University of Surrey, Guildford GU2 7XH, UK;
- National Physical Laboratory, London TW11 0LW, UK
| | - Roberto M. La Ragione
- School of Veterinary Medicine, University of Surrey, Guildford GU2 7AL, UK; (A.M.); (R.M.L.R.)
- School of Biosciences, University of Surrey, Guildford GU2 7XH, UK
| | - Kevin Wells
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, UK;
- Surrey DataHub, University of Surrey, Guildford GU2 7AL, UK
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Li Z, Li X, Wu W, Lyu H, Tang X, Zhou C, Xu F, Luo B, Jiang Y, Liu X, Xiang W. A novel dilated contextual attention module for breast cancer mitosis cell detection. Front Physiol 2024; 15:1337554. [PMID: 38332988 PMCID: PMC10850563 DOI: 10.3389/fphys.2024.1337554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 01/03/2024] [Indexed: 02/10/2024] Open
Abstract
Background and object: Mitotic count (MC) is a critical histological parameter for accurately assessing the degree of invasiveness in breast cancer, holding significant clinical value for cancer treatment and prognosis. However, accurately identifying mitotic cells poses a challenge due to their morphological and size diversity. Objective: We propose a novel end-to-end deep-learning method for identifying mitotic cells in breast cancer pathological images, with the aim of enhancing the performance of recognizing mitotic cells. Methods: We introduced the Dilated Cascading Network (DilCasNet) composed of detection and classification stages. To enhance the model's ability to capture distant feature dependencies in mitotic cells, we devised a novel Dilated Contextual Attention Module (DiCoA) that utilizes sparse global attention during the detection. For reclassifying mitotic cell areas localized in the detection stage, we integrate the EfficientNet-B7 and VGG16 pre-trained models (InPreMo) in the classification step. Results: Based on the canine mammary carcinoma (CMC) mitosis dataset, DilCasNet demonstrates superior overall performance compared to the benchmark model. The specific metrics of the model's performance are as follows: F1 score of 82.9%, Precision of 82.6%, and Recall of 83.2%. With the incorporation of the DiCoA attention module, the model exhibited an improvement of over 3.5% in the F1 during the detection stage. Conclusion: The DilCasNet achieved a favorable detection performance of mitotic cells in breast cancer and provides a solution for detecting mitotic cells in pathological images of other cancers.
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Affiliation(s)
- Zhiqiang Li
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan, China
| | - Xiangkui Li
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China
| | - Weixuan Wu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan, China
| | - He Lyu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan, China
| | - Xuezhi Tang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan, China
| | - Chenchen Zhou
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan, China
| | - Fanxin Xu
- Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Bin Luo
- Sichuan Huhui Software Co., LTD., Mianyang, Sichuan, China
| | - Yulian Jiang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan, China
| | - Xingwen Liu
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan, China
| | - Wei Xiang
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan, China
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Lee J, Cha S, Kim J, Kim JJ, Kim N, Jae Gal SG, Kim JH, Lee JH, Choi YD, Kang SR, Song GY, Yang DH, Lee JH, Lee KH, Ahn S, Moon KM, Noh MG. Ensemble Deep Learning Model to Predict Lymphovascular Invasion in Gastric Cancer. Cancers (Basel) 2024; 16:430. [PMID: 38275871 PMCID: PMC10814827 DOI: 10.3390/cancers16020430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024] Open
Abstract
Lymphovascular invasion (LVI) is one of the most important prognostic factors in gastric cancer as it indicates a higher likelihood of lymph node metastasis and poorer overall outcome for the patient. Despite its importance, the detection of LVI(+) in histopathology specimens of gastric cancer can be a challenging task for pathologists as invasion can be subtle and difficult to discern. Herein, we propose a deep learning-based LVI(+) detection method using H&E-stained whole-slide images. The ConViT model showed the best performance in terms of both AUROC and AURPC among the classification models (AUROC: 0.9796; AUPRC: 0.9648). The AUROC and AUPRC of YOLOX computed based on the augmented patch-level confidence score were slightly lower (AUROC: -0.0094; AUPRC: -0.0225) than those of the ConViT classification model. With weighted averaging of the patch-level confidence scores, the ensemble model exhibited the best AUROC, AUPRC, and F1 scores of 0.9880, 0.9769, and 0.9280, respectively. The proposed model is expected to contribute to precision medicine by potentially saving examination-related time and labor and reducing disagreements among pathologists.
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Affiliation(s)
- Jonghyun Lee
- Department of Medical and Digital Engineering, Hanyang University College of Engineering, Seoul 04763, Republic of Korea;
| | - Seunghyun Cha
- Department of Pre-Medicine, Chonnam National University Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Gwangju 58128, Republic of Korea;
| | - Jiwon Kim
- NetTargets, 495 Sinseong-dong, Yuseong, Daejeon 34109, Republic of Korea
| | - Jung Joo Kim
- AMGINE, Inc., Jeongui-ro 8-gil 13, Seoul 05836, Republic of Korea;
| | - Namkug Kim
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 25440, Republic of Korea; (N.K.); (S.G.J.G.)
| | - Seong Gyu Jae Gal
- Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 25440, Republic of Korea; (N.K.); (S.G.J.G.)
| | - Ju Han Kim
- Division of Biomedical Informatics, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Jeong Hoon Lee
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305-5101, USA;
| | - Yoo-Duk Choi
- Department of Pathology, Chonnam National University Medical School, Gwangju 61469, Republic of Korea;
| | - Sae-Ryung Kang
- Department of Nuclear Medicine, Clinical Medicine Research Center, Chonnam National University Hospital, 671 Jebongno, Gwangju 61469, Republic of Korea;
| | - Ga-Young Song
- Departments of Hematology-Oncology, Chonnam National University Hwasun Hospital, 322 Seoyangro, Hwasun 58128, Republic of Korea; (G.-Y.S.); (D.-H.Y.)
| | - Deok-Hwan Yang
- Departments of Hematology-Oncology, Chonnam National University Hwasun Hospital, 322 Seoyangro, Hwasun 58128, Republic of Korea; (G.-Y.S.); (D.-H.Y.)
| | - Jae-Hyuk Lee
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
| | - Kyung-Hwa Lee
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
| | - Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
| | - Kyoung Min Moon
- Division of Pulmonary and Allergy Medicine, Department of Internal Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea
- Artificial Intelligence, ZIOVISION Co., Ltd., Chuncheon 24341, Republic of Korea
| | - Myung-Giun Noh
- Department of Pathology, Chonnam National University Hwasun Hospital and Medical School, 322 Seoyang-ro, Hwasun-eup, Hwasun-gun, Hwasun 58128, Republic of Korea (K.-H.L.)
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