Texture-based deep learning for effective histopathological cancer image classification
NZ Tsaku, SC Kosaraju, T Aqila… - 2019 IEEE …, 2019 - ieeexplore.ieee.org
Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has
been highlighted along with the advancements in microscopic imaging techniques, since …
been highlighted along with the advancements in microscopic imaging techniques, since …
Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features
Background Histopathology image analysis is a gold standard for cancer recognition and
diagnosis. Automatic analysis of histopathology images can help pathologists diagnose …
diagnosis. Automatic analysis of histopathology images can help pathologists diagnose …
Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks
Histopathological images are widely used for the analysis of diseased (tumor) tissues and
patient treatment selection. While the majority of microscopy image processing was …
patient treatment selection. While the majority of microscopy image processing was …
Patch-level tumor classification in digital histopathology images with domain adapted deep learning
Tumor histopathology is a crucial step in cancer diagnosis which involves visual inspection
of imaging data to detect the presence of tumor cells among healthy tissues. This manual …
of imaging data to detect the presence of tumor cells among healthy tissues. This manual …
Optimization of CNN model with hyper parameter tuning for enhancing sturdiness in classification of histopathological images
A Johny, DM KN, DTJ Nallikuzhy - Proceedings of the 2nd …, 2020 - papers.ssrn.com
The field of pathology has advanced so rapidly that it is now possible to produce whole slide
images (WSI) from glass slides with digital scanners producing high-quality images. Image …
images (WSI) from glass slides with digital scanners producing high-quality images. Image …
Toward large-scale histopathological image analysis via deep learning
The histopathological image has served as the gold standard of cancer diagnosis and
played a vital role in diagnosing cancer in clinical settings for over a century. Traditionally …
played a vital role in diagnosing cancer in clinical settings for over a century. Traditionally …
Texture CNN for histopathological image classification
J de Matos, A de Souza Britto… - 2019 IEEE 32nd …, 2019 - ieeexplore.ieee.org
Biopsies are the gold standard for breast cancer diagnosis. This task can be improved by the
use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and …
use of Computer Aided Diagnosis (CAD) systems, reducing the time of diagnosis and …
Survey of Recent Deep Neural Networks with Strong Annotated Supervision in Histopathology
D Petríková, I Cimrák - Computation, 2023 - mdpi.com
Deep learning (DL) and convolutional neural networks (CNNs) have achieved state-of-the-
art performance in many medical image analysis tasks. Histopathological images contain …
art performance in many medical image analysis tasks. Histopathological images contain …
MPSA: Multi-Position Supervised Soft Attention-based convolutional neural network for histopathological image classification
B Qing, S Zhanquan, W Kang, W Chaoli… - Expert Systems with …, 2024 - Elsevier
In recent years, significant achievements have been made in the field of histopathological
image analysis using convolutional neural networks (CNNs). However, existing CNNs fail to …
image analysis using convolutional neural networks (CNNs). However, existing CNNs fail to …
Multi-stage pathological image classification using semantic segmentation
Histopathological image analysis is an essential process for the discovery of diseases such
as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigapixel …
as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigapixel …