ITF-WPI: Image and text based cross-modal feature fusion model for wolfberry pest recognition
As one of the necessary cash crops in China and many other countries, wolfberry is
parasitized by multiple pests, and its yield is highly susceptible to being affected. On the …
parasitized by multiple pests, and its yield is highly susceptible to being affected. On the …
SAMPLER: unsupervised representations for rapid analysis of whole slide tissue images
P Mukashyaka, TB Sheridan, JH Chuang - EBioMedicine, 2024 - thelancet.com
Background Deep learning has revolutionized digital pathology, allowing automatic analysis
of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs …
of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. WSIs …
RDTNet: A residual deformable attention based transformer network for breast cancer classification
DR Nayak - Expert Systems with Applications, 2024 - Elsevier
Accurate and timely detection of breast cancer plays a pivotal role in reducing the mortality
rate. Deep learning models, especially CNNs, have recently shown astounding performance …
rate. Deep learning models, especially CNNs, have recently shown astounding performance …
Intelligent ultrasound imaging for enhanced breast cancer diagnosis: Ensemble transfer learning strategies
According to WHO statistics for 2018, there are 1.2 million cases and 700,000 deaths from
breast cancer (BC) each year, making it the second-highest cause of mortality for women …
breast cancer (BC) each year, making it the second-highest cause of mortality for women …
Fusing global context with multiscale context for enhanced breast cancer classification
Breast cancer is the second most common type of cancer among women. Prompt detection
of breast cancer can impede its advancement to more advanced phases, thereby elevating …
of breast cancer can impede its advancement to more advanced phases, thereby elevating …
Fine tuning deep learning models for breast tumor classification
This paper proposes an approach to enhance the differentiation task between benign and
malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The …
malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The …
[HTML][HTML] Tumor detection in breast cancer pathology patches using a Multi-scale Multi-head Self-attention Ensemble Network on Whole Slide Images
R Ge, G Chen, K Saruta, Y Terata - Machine Learning with Applications, 2024 - Elsevier
Breast cancer (BC) is the most common type of cancer among women globally and is one of
the leading causes of cancer-related deaths among women. In the diagnosis of BC …
the leading causes of cancer-related deaths among women. In the diagnosis of BC …
Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples
Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-
invasive techniques like ultrasound and mammogram are used for diagnosis, histological …
invasive techniques like ultrasound and mammogram are used for diagnosis, histological …
SwinGALE: fusion of swin transformer and attention mechanism for GAN-augmented liver tumor classification with enhanced deep learning
Liver diseases represent a significant challenge to global healthcare systems, necessitating
accurate and timely diagnosis for effective intervention. However, the intricate nature of liver …
accurate and timely diagnosis for effective intervention. However, the intricate nature of liver …
Two-level content-based mammogram retrieval using the ACR BI-RADS assessment code and learning-driven distance selection
A Jouirou, I Souissi, W Barhoumi - The Journal of Supercomputing, 2024 - Springer
Content-based mammogram retrieval (CBMR) is an effective approach to assist radiologists
in diagnosing patients' mammograms. Indeed, by studying the similar cases to diagnostic …
in diagnosing patients' mammograms. Indeed, by studying the similar cases to diagnostic …