Adversarial Attacks on Deep Neural Network: Developing Robust Models Against Evasion Technique
In the fast-paced field of machine learning, it is important to build agile models that can
correctly classify data in the face of enemy attacks. This paper explores the field of …
correctly classify data in the face of enemy attacks. This paper explores the field of …
Application of deep learning in histopathology images of breast cancer: a review
With the development of artificial intelligence technology and computer hardware functions,
deep learning algorithms have become a powerful auxiliary tool for medical image analysis …
deep learning algorithms have become a powerful auxiliary tool for medical image analysis …
A comprehensive survey of intestine histopathological image analysis using machine vision approaches
Y Jing, C Li, T Du, T Jiang, H Sun, J Yang, L Shi… - Computers in Biology …, 2023 - Elsevier
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is
the third most common malignancy and the fourth leading cause of cancer death worldwide …
the third most common malignancy and the fourth leading cause of cancer death worldwide …
[HTML][HTML] Digitization of pathology labs: a review of lessons learned
Pathology laboratories are increasingly using digital workflows. This has the potential of
increasing lab efficiency, but the digitization process also involves major challenges …
increasing lab efficiency, but the digitization process also involves major challenges …
DAS-MIL: Distilling Across Scales for MIL classification of histological WSIs
Abstract The adoption of Multi-Instance Learning (MIL) for classifying Whole-Slide Images
(WSIs) has increased in recent years. Indeed, pixel-level annotation of gigapixel WSI is …
(WSIs) has increased in recent years. Indeed, pixel-level annotation of gigapixel WSI is …
Sac-net: enhancing spatiotemporal aggregation in cervical histological image classification via label-efficient weakly supervised learning
Cervical cancer is the fourth most common cancer in women and its subtyping requires
examining histopathological slides or digital images, such as whole slide images (WSIs) …
examining histopathological slides or digital images, such as whole slide images (WSIs) …
The rise of ai language pathologists: Exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification
This paper introduces the novel concept of few-shot weakly supervised learning for
pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed …
pathology Whole Slide Image (WSI) classification, denoted as FSWC. A solution is proposed …
NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images
Simple Summary Recent advancements in AI have revolutionized cancer research,
especially in the analysis of histopathological imaging data with minimal human …
especially in the analysis of histopathological imaging data with minimal human …
Multi-prototype few-shot learning in histopathology
J Deuschel, D Firmbach, CI Geppert… - Proceedings of the …, 2021 - openaccess.thecvf.com
The ability to adapt quickly to a new task or data distribution based on only a few examples
is a challenge in AI and highly relevant for various domains. In digital pathology, slight …
is a challenge in AI and highly relevant for various domains. In digital pathology, slight …
LPCANet: Classification of laryngeal cancer histopathological images using a CNN with position attention and channel attention mechanisms
Laryngeal cancer is one of the most common malignant tumors in otolaryngology, and
histopathological image analysis is the gold standard for the diagnosis of laryngeal cancer …
histopathological image analysis is the gold standard for the diagnosis of laryngeal cancer …