Stochastic Class-Attention Net to Detect the Breast Carcinoma Subtypes With Test Time Augmentation

Despite advances in medical sciences, breast cancer remains a deadly disease globally, primarily affecting women. Fortunately, studies claim that breast cancer is treatable if diagnosed early. Late diagnoses have poor prognoses and can affect the patient's quality of life. Therefore, a significant research body is dedicated to establishing and identifying the disease at an initial stage. Deep learning (DL) techniques are garnering attention for aiding medical professionals in detecting this disease using histopathology (HP) image modality. The heterogeneous nature of this disease subtypes results in the imbalances of benign and malignant subtypes. From a DL point of view, this becomes an imbalanced problem deserving special care. Unfortunately, current DL-based techniques do not fully address this issue and suffer from poor metrics and robustness. In this work, we present a DL-based breast cancer automatic detection system (BCADS) using a novel architecture stochastic class-attention net (SCAN). This technique performed better when combined with label smoothing and test time augmentation. This work outperforms the previously reported results for binary and multiclass on the BreaKHis dataset. Also, we validated our method on separate BACH and BCNB datasets to prove its effectiveness and clinical relevancy. We hope that the designed BCADS will help the treating doctor and pathologist in a meaningful way and thus help to reduce the impact of this deadly disease.