Mitosis assessment of breast cancer has a strong prognostic importance and is visually evaluated by pathologists. The inter, and intra-observer variability of this assessment is high. In this paper, a two-stage deep learning approach, named MITNET, has been applied to automatically detect nucleus and classify mitoses in whole slide images (WSI) of breast cancer. Moreover, this paper introduces two new datasets. The first dataset is used to detect the nucleus in the WSIs, which contains 139,124 annotated nuclei in 1749 patches extracted from 115 WSIs of breast cancer tissue, and the second dataset consists of 4908 mitotic cells and 4908 non-mitotic cells image samples extracted from 214 WSIs which is used for mitosis classification. The created datasets are used to train the MITNET network, which consists of two deep learning architectures, called MITNET-det and MITNET-rec, respectively, to isolate nuclei cells and identify the mitoses in WSIs. In MITNET-det architecture, to extract features from nucleus images and fuse them, CSPDarknet and Path Aggregation Network (PANet) are used, respectively, and then, a detection strategy using You Look Only Once (scaled-YOLOv4) is employed to detect nucleus at three different scales. In the classification part, the detected isolated nucleus images are passed through proposed MITNET-rec deep learning architecture, to identify the mitosis in the WSIs. Various deep learning classifiers and the proposed classifier are trained with a publicly available mitosis datasets (MIDOG and ATYPIA) and then, validated over our created dataset. The results verify that deep learning-based classifiers trained on MIDOG and ATYPIA have difficulties to recognize mitosis on our dataset which shows that the created mitosis dataset has unique features and characteristics. Besides this, the proposed classifier outperforms the state-of-the-art classifiers significantly and achieves a $$68.7\%$$ 68.7 % F1-score and $$49.0\%$$ 49.0 % F1-score on the MIDOG and the created mitosis datasets, respectively. Moreover, the experimental results reveal that the overall proposed MITNET framework detects the nucleus in WSIs with high detection rates and recognizes the mitotic cells in WSI with high F1-score which leads to the improvement of the accuracy of pathologists’ decision.

The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aided patch-based deep learning tubule segmentation framework, named Tubule-U-Net, is developed and proposed to segment tubules in Whole Slide Images (WSI) of breast cancer. Moreover, this paper presents a new tubule segmentation dataset consisting of 30820 polygonal annotated tubules in 8225 patches. The Tubule-U-Net framework first uses a patch enhancement technique such as reflection or mirror padding and then employs an asymmetric encoder-decoder semantic segmentation model. The encoder is developed in the model by various deep learning architectures such as EfficientNetB3, ResNet34, and DenseNet161, whereas the decoder is similar to U-Net. Thus, three different models are obtained, which are EfficientNetB3-U-Net, ResNet34-U-Net, and DenseNet161-U-Net. The proposed framework with three different models, U-Net, U-Net++, and Trans-U-Net segmentation methods are trained on the created dataset and tested on five different WSIs. The experimental results demonstrate that the proposed framework with the EfficientNetB3 model trained on patches obtained using the reflection padding and tested on patches with overlapping provides the best segmentation results on the test data and achieves 95.33%, 93.74%, and 90.02%, dice, recall, and specificity scores, respectively.

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e13607 Background: The Nottingham histologic grade (NHG) is a strong prognostic factor in early-stage breast cancer. It consists of nuclear pleomorphism, tubular formation, and mitotic count. We recently developed an artificial intelligence (AI) based automatic grading system. Methods: In this study, we have retrospectively evaluated 179,651 hematoxylin and eosin-stained patches extracted from 402 digitized biopsies of 338 patients with confirmed invasive ductal carcinoma diagnosis. The data was collected at Acıbadem University Hospital between 2017 and 2021. The digitized slides were manually labeled by seven pathologists before being used to train the deep learning models (DL). The pre-trained (on ImageNet) DL architectures which are EfficientNet backbone U-Net, YOLOv5, DenseNet161, and a modified VGG-11 have been fine-tuned with this study’s dataset for the tubule segmentation, nuclei detection, mitosis classification, and the nuclear pleomorphism classification tasks, respectively. Data augmentation for boosting the accuracy was done. Semantic segmentation for tubular formation, object detection for detection of nuclei, and image classification for the mitotic count and nuclear pleomorphism was also performed. Results: The AI-based algorithms have obtained reproducible histologic scores with mean F1 scores, sensitivities, and specificities as presented in Table. Conclusions: The AI-based automatic grading system is accurate in the evaluation of the components of NHG. This system will speed up the pathology workflow in the clinic, provide decision support to pathologists and mitigate the sensitivity associated with the traditional grading process. [Table: see text]