article · Computational Intelligence and Neuroscience
Histopathological evaluation for breast cancer detection requires substantial clinical experience and time from pathologists. Breast tissue contains diverse structures with wide variations in textural intensity, making the identification of abnormalities difficult. Artificial intelligence has the potential to support specialists by delivering more accurate and efficient diagnostic outcomes, functioning as a computer-assisted system that offers a reliable second opinion. However, deep learning models conventionally demand large numbers of labelled images for effective training. To tackle this constraint in breast cancer histopathology classification, a self-training learning framework using a deep neural network with residual learning was designed. The resulting architecture was constructed from scratch and trained to perform image classification without requiring extensive manually labelled datasets.
Accurate breast cancer diagnosis via tissue imaging demands extensive specialist time and effort. Developing machine learning solutions that do not depend on vast libraries of manually labelled images simplifies algorithm training. This can accelerate the creation of diagnostic support systems that assist clinicians, potentially improving diagnostic consistency and reducing the workload on medical staff.
The method is intended for computer-assisted diagnostic software used by pathologists and radiologists reviewing breast tissue images. Potential commercial partners include diagnostic software vendors and clinical imaging laboratories. Because the abstract details only the construction and initial training of the model without reporting clinical validation metrics, the technology is at an early research stage.
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Pathologists need a lot of clinical experience and time to do the histopathological investigation. AI may play a significant role in supporting pathologists and resulting in more accurate and efficient histopathological diagnoses. Breast cancer is one of the most diagnosed cancers in women worldwide. Breast cancer may be detected and diagnosed using imaging methods such as histopathological images. Since various tissues make up the breast, there is a wide range of textural intensity, making abnormality detection difficult. As a result, there is an urgent need to improve computer-assisted systems (CAD) that can serve as a second opinion for radiologists when they use medical images. A self-training learning method employing deep learning neural network with residual learning is proposed to overcome the issue of needing a large number of labeled images to train deep learning models in breast cancer histopathology image classification. The suggested model is built from scratch and trained.
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DOI: 10.1155/2022/9086060
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