A cluster attention-based multiple instance learning network for enhancing histopathological image interpretation.

Background: Histopathological diagnosis involves examining abnormal architectural patterns and cellular-level changes. Whole slide images (WSIs) provide comprehensive digital representations of tissue samples, enabling detailed analysis and interpretation. Annotating the giga-pixel images remains labor-intensive, requiring experts to label abnormal patterns and cellular changes.

Automated Screening of Precancerous Cervical Cells Through Contrastive Self-Supervised Learning.

Cervical cancer is a significant health challenge, yet it can be effectively prevented through early detection. Cytology-based screening is critical for identifying cancerous and precancerous lesions; however, the process is labor-intensive and reliant on trained experts to scan through hundreds of thousands of mostly normal cells. To address these challenges, we propose a novel distribution-augmented approach using contrastive self-supervised learning for detecting abnormal squamous cervical cells from cytological images.

Toward Interpretable Cell Image Representation and Abnormality Scoring for Cervical Cancer Screening Using Pap Smears.

Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to the class imbalance problems and high-labeling costs that are both prevalent in healthcare.

Improving Tumor-Infiltrating Lymphocytes Score Prediction in Breast Cancer with Self-Supervised Learning.

Tumor microenvironment (TME) plays a pivotal role in immuno-oncology, which investigates the intricate interactions between tumors and the human immune system. Specifically, tumor-infiltrating lymphocytes (TILs) are crucial biomarkers for evaluating the prognosis of breast cancer patients and have the potential to refine immunotherapy precision and accurately identify tumor cells in specific cancer types. In this study, we conducted tissue segmentation and lymphocyte detection tasks to predict TIL scores by employing self-supervised learning (SSL) model-based approaches capable of addressing limited labeling data issues.

Deep Learning Prediction of Promoter Mutation Status in Thyroid Cancer Using Histologic Images.

objectives: Telomerase reverse transcriptase () promoter mutation, found in a subset of patients with thyroid cancer, is strongly associated with aggressive biologic behavior. Predicting promoter mutation is thus necessary for the prognostic stratification of thyroid cancer patients. In this study, we evaluate promoter mutation status in thyroid cancer through the deep learning approach using histologic images.