Latest AI and machine learning research in oncology/hematology for healthcare professionals.
Existing methods for multimodal MRI segmentation with missing modalities typically assume that all MRI modalities are available during training. However, in clinical practice, some modalities may be missing due to the sequential nature of MRI acquisition, leading to performance degradation. Furthermore, retraining models to accommodate newly available modalities can be inefficient and may cause ...
This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components necessary for optimal performance.We systematically analyze the impact of input and dose grid resolution, input type, loss function, model architecture,...
Atherosclerosis is a chronic inflammatory disease, this study aims to investigate the immune landscape in carotid atherosclerotic plaque formation and...
Current brain tumor segmentation methods often utilize a U-Net architecture based on efficient convolutional neural networks. While effective, these a...
Foundation models (FMs) such as CLIP and SAM have recently shown great promise in image segmentation tasks, yet their adaptation to 3D medical imagi...
Chromosome analysis is vital for diagnosing genetic disorders and guiding cancer therapy decisions through the identification of somatic clonal aber...
Cervical cancer, the fourth leading cause of cancer in women globally, requires early detection through Pap smear tests to identify precancerous cha...
BACKGROUND: Esophageal cancer is the sixth most common cancer worldwide, with a high mortality rate. Early prognosis of esophageal abnormalities can i...
In this article we comment on the paper by Xu describing retrospective data on endoscopic treatment outcome of esophageal gastrointestinal stromal tu...
BACKGROUND: Generative artificial intelligence (AI) chatbots may be useful tools for supporting shared prostate cancer (PrCA) screening decisions, but...
The precise and timely diagnosis of brain tumors is essential for accelerating patient recovery and preserving lives. Brain tumors exhibit a variety o...
Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tu...
The radiological dosimetric parameters and clinical features were screened by machine learning to construct a prediction model for the short-term effi...
Artificial intelligence (AI) holds strong potential for medical diagnostics, yet its clinical adoption is limited by a lack of interpretability and ...
Accurately tracking particles and determining their position along the optical axis is a major challenge in optical microscopy, especially when extr...
Triple-negative breast cancer (TNBC) is an aggressive subtype defined by the lack of estrogen receptor (ER), progesterone receptor (PR), and human e...
Background: Deep learning has potential to improve the efficiency and consistency of radiation therapy planning, but clinical adoption is hindered b...
OBJECTIVES: To explore the association of the expression of WW domain-containing ubiquitin E3 ligase 1 (WWP1) with immune infiltration in tumor microe...
Early prevention and standardized management of refractory wounds in the elderly are very important for improving prognosis, reducing disability rate,...
Polygonati Rhizoma, a traditional Chinese medicine, has demonstrated immunomodulatory and anticancer properties, yet its precise mechanisms in stomach...