Latest AI and machine learning research in breast cancer for healthcare professionals.
Domain shift is a critical problem for pathology AI as pathology data is heavily influenced by center-specific conditions. Current pathology domain adaptation methods focus on image patches rather than WSI, thus failing to capture global WSI features required in typical clinical scenarios. In this work, we address the challenges of slide-level domain shift by proposing a Hierarchical Adaptation ...
Complete removal of cancer tumors with a negative specimen margin during lumpectomy is essential in reducing breast cancer recurrence. However, 2D specimen radiography (SR), the current method used to assess intraoperative specimen margin status, has limited accuracy, resulting in nearly a quarter of patients requiring additional surgery. To address this, we propose a novel deep learning framewo...
Cone-beam computed tomography (CBCT) is widely used in dentistry, surgery, radiotherapy and other medical fields. However, repeated CBCT scans expose ...
Inflammation is increasingly recognized as a critical factor in acute myeloid leukemia (AML) pathogenesis. We performed blood-based proteomic profilin...
Epithelial ovarian cancer remains one of the deadliest gynecologic malignancies, with late-stage diagnosis, high recurrence rates, and resistance to p...
PURPOSE: This study aimed to develop a machine learning (ML) model to predict bloodstream infection (BSI) in chemotherapy patients.
Dental diagnosis relies on two primary imaging modalities: panoramic radiographs (PX) providing 2D oral cavity representations, and Cone-Beam Comput...
RNA interference (RNAi) has emerged as a transformative approach for cancer therapy, enabling precise gene silencing through small interfering RNA (si...
Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring criti...
Background: Radiation-induced leukopenia caused by low-dose exposure is frequently associated with Traditional Chinese Medicine (TCM) syndromes like...
High-resolution computed tomography (CT) imaging is essential for medical diagnosis but requires increased radiation exposure, creating a critical t...
PURPOSE: Although radiotherapy (RT) is one of the primary treatment modalities used in the treatment of cancer, patients often experience toxicity dur...
Cardiac substructures are essential in thoracic radiation therapy planning to minimize risk of radiation-induced heart disease. Deep learning (DL) o...
With the rapid development of digital pathology, virtual staining has become a key technology in multimedia medical information systems, offering ne...
Cancer subtype classification is crucial for personalized treatment and prognostic assessment. However, effectively integrating multi-omic data rema...
Time-resolved CT is an advanced measurement technique that has been widely used to observe dynamic objects, including periodically varying structure...
Boolean networks are powerful frameworks for capturing the logic of gene-regulatory circuits, yet their combinatorial explosion hampers exhaustive a...
Radiologists are witnessing astonishing innovation and advancement of CT technologies and their clinical applications. This review highlights how phot...
Cancer pain management (CPM) is crucial in oncology care, with current approaches including pharmacotherapy, radiotherapy, chemotherapy, nerve blocks,...
BACKGROUND: The tangential field-in-field (FIF) technique is a widely used method in breast radiation therapy, known for its efficiency and the reduce...