CLNAD-Net: A Multi-Task Learning Framework for Cervical Lymph Node Computer-Aided Diagnosis Network.

ViT-radiomics fusion for lymph node classification in ultrasound images: a multicenter study.

BACKGROUND Ultrasound (US) is widely used for assessing lymph node (LN) status, but its diagnostic accuracy remains highly operator dependent. A robust computer-assisted diagnostic model may enhance clinical performance and improve inter-operator and inter-center consistencies. PURPOSE To develop and validate a multi...

MulGM-Net: A decision-based gated memory network with contrastive learning for multi-task cancer diagnosis

Experiments on two datasets suggest the potential utility of MulGM-Net for joint tumor delineation and malignancy assessment in head-and-neck and breast imaging, while the limited dataset size and the absence of external validation indicate that broader confirmation is still required.

ACHS-CervNet: A Hybrid Stacked Deep Learning Approach for Accurate Cervical Cancer Detection and Classification

Cervical cancer remains one of the leading causes of mortality among women worldwide, and early, accurate detection is crucial for effective treatment. Conventional cytological screening suffers from inter-observer variability and limited accuracy, prompting increased adoption of artificial intelligence, Machine Learni...

Design and Development of Hybrid Learning Model for the detection of Ovarian Cancers

Ovarian cancer remains as a significant contributor to female mortality across the globe, mainly because it has a difficulty in obtaining an early and precise diagnosis through complex histopathology images. Automated image-based diagnosis has therefore become essential to assist pathologists in making effective diagno...

An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy

The proposed IMIL-Net provides a high-accuracy, interpretable, and clinically-aligned diagnostic solution by analyzing complete patient examinations and providing a statistically validated decision-making process, and represents a trustworthy tool for integration into the clinical otolaryngology workflow.

Multi-class lung cancer detection and classification from CT scans using CNN transformer