[HTML][HTML] Lung nodule diagnosis and cancer histology classification from computed tomography data by convolutional neural networks: A survey
Lung cancer is among the deadliest cancers. Besides lung nodule classification and
diagnosis, develo** non-invasive systems to classify lung cancer histological …
diagnosis, develo** non-invasive systems to classify lung cancer histological …
A generalized framework of feature learning enhanced convolutional neural network for pathology-image-oriented cancer diagnosis
In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is
proposed for cancer detection from histopathology images. To build a highly generalized …
proposed for cancer detection from histopathology images. To build a highly generalized …
Artificial Intelligence Applications in Lymphoma Diagnosis and Management: Opportunities, Challenges, and Future Directions
M Shen, Z Jiang - Journal of Multidisciplinary Healthcare, 2024 - Taylor & Francis
Lymphoma, a heterogeneous group of blood cancers, presents significant diagnostic and
therapeutic challenges due to its complex subtypes and variable clinical outcomes. Artificial …
therapeutic challenges due to its complex subtypes and variable clinical outcomes. Artificial …
Learning how to detect: A deep reinforcement learning method for whole-slide melanoma histopathology images
Cutaneous melanoma represents one of the most life-threatening malignancies.
Histopathological image analysis serves as a vital tool for early melanoma detection. Deep …
Histopathological image analysis serves as a vital tool for early melanoma detection. Deep …
A novel approach for human diseases prediction using nature inspired computing & machine learning approach
Globally, patients with diabetes, diabetic retinopathy, cancer, and heart disease are growing
rapidly in developed and develo** countries. As a result of these ailments, the rate of …
rapidly in developed and develo** countries. As a result of these ailments, the rate of …
GestroNet: a framework of saliency estimation and optimal deep learning features based gastrointestinal diseases detection and classification
In the last few years, artificial intelligence has shown a lot of promise in the medical domain
for the diagnosis and classification of human infections. Several computerized techniques …
for the diagnosis and classification of human infections. Several computerized techniques …
Pyramid-based self-supervised learning for histopathological image classification
Large-scale labeled datasets are crucial for the success of supervised learning in medical
imaging. However, annotating histopathological images is a time-consuming and labor …
imaging. However, annotating histopathological images is a time-consuming and labor …
[HTML][HTML] Invasion depth estimation of carcinoma cells using adaptive stain normalization to improve epidermis segmentation accuracy
Submucosal invasion depth is a significant prognostic factor when assessing lymph node
metastasis and cancer itself to plan proper treatment for the patient. Conventionally …
metastasis and cancer itself to plan proper treatment for the patient. Conventionally …
A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma
This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of
lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological …
lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological …
A heteromorphous deep CNN framework for medical image segmentation using local binary pattern
Estimating mitotic nuclei in breast cancer samples can aid in determining the tumor's
aggressiveness and grading system. Because of their strong resemblance to non-mitotic …
aggressiveness and grading system. Because of their strong resemblance to non-mitotic …