Hormone receptor status is determined primarily to identify breast cancer patients who may benefit from hormonal therapy. The current clinical practice for the testing using either Allred score or H-score is still based on laborious manual counting and estimation of the amount and intensity of positively stained cancer cells in immunohistochemistry (IHC)-stained slides. This work integrates cell detection and classification workflow for breast carcinoma estrogen receptor (ER)-IHC-stained images and presents an automated evaluation system. The system first detects all cells within the specific regions and classifies them into negatively, weakly, moderately, and strongly stained, followed by Allred scoring for ER status evaluation. The generated Allred score relies heavily on accurate cell detection and classification and is compared against pathologists’ manual estimation. Experiments on 40 whole-slide images show 82.5% agreement on hormonal treatment recommendation, which we believe could be further improved with an advanced learning model and enhancement to address the cases with 0% ER status. This promising system can automate the exhaustive exercise to provide fast and reliable assistance to pathologists and medical personnel. The system has the potential to improve the overall standards of prognostic reporting for cancer patients, benefiting pathologists, patients, and also the public at large.

Breast cancer remains a leading cause of morbidity and mortality worldwide. Histopathology, particularly the analysis of nuclear morphology in tissue samples, is critical for diagnosing and understanding the progression of breast cancer. Accurate nuclei segmentation plays a pivotal role in enabling detailed assessment of nuclear size, shape, and distribution patterns, which are essential for clinical diagnosis. However, traditional single-scale segmentation methods often fail to achieve this accuracy due to their inability to preserve fine details, capture broader contextual information, distinguish overlapping nuclei, and handle the inherent variability in nuclear morphology across different cell types. To address these challenges, we propose a multi-stream encoder-decoder architecture named M3-Net++ (Multi-Scale, Multi-Level, Multi-Stream Network), a novel deep learning model tailored for nuclei segmentation in histopathology images. M3-Net++ integrates both global and local tissue features for improved segmentation accuracy. The inclusion of Feature Refinement and Redundancy Elimination (FRRE) module further emphasizes critical features, while skip connections preserve high-resolution spatial information. To overcome challenges such as overlapping nuclei and class imbalance, we introduce a Hybrid Segmentation Loss (HSL) function. M3-Net++ achieves state-of-the-art performance on three benchmark datasets-ER-IHC, MoNuSAC, and CoNSeP-achieving Dice scores of 0.875, 0.871, and 0.872, and Panoptic Quality (PQ) scores of 0.730, 0.700, and 0.588, respectively, outperforming models such as HoVer-Net and SMILE. Despite these improvements, M3-Net++ remains computationally efficient, requiring only 46.18M parameters, 7.2 GB of peak GPU memory, and 0.119 s of inference time per 256×256 patch. These results highlight the robustness, adaptability, and clinical potential of M3-Net++ for breast cancer histopathology image analysis.

Computer-aided systems can assist doctors in detecting cancer at an early stage using medical image analysis. In estrogen receptor immunohistochemistry (ER-IHC)-stained whole-slide images, automated cell identification and segmentation are helpful in the prediction scoring of hormone receptor status, which aids pathologists in determining whether to recommend hormonal therapy or other therapies for a patient. Accurate scoring can be achieved with accurate segmentation and classification of the nuclei. This paper presents two main objectives: first is to identify the top three models for this classification task and establish an ensemble model, all using 10-fold cross-validation strategy; second is to detect recurring misclassifications within the dataset to identify “misclassified nuclei” or “incorrectly labeled nuclei” for the nuclei class ground truth. The classification task is carried out using 32 pre-trained deep learning models from Keras Applications, focusing on their effectiveness in classifying negative, weak, moderate, and strong nuclei in the ER-IHC histopathology images. An ensemble learning with logistic regression approach is employed for the three best models. The analysis reveals that the top three performing models are EfficientNetB0, EfficientNetV2B2, and EfficientNetB4 with an accuracy of 94.37%, 94.36%, and 94.29%, respectively, and the ensemble model’s accuracy is 95%. We also developed a web-based platform for the pathologists to rectify the “faulty-class” nuclei in the dataset. The complete flow of this work can benefit the field of medical image analysis especially when dealing with intra-observer variability with a large number of images for ground truth validation.