Related papers: Deep Learning Techniques for Cervical Cancer Diagn…
Advances in deep learning for natural images have prompted a surge of interest in applying similar techniques to medical images. The majority of the initial attempts focused on replacing the input of a deep convolutional neural network with…
A comprehensive study on machine and deep learning techniques for classification of normal and abnormal cervical cells by using pap smear images from Herlev dataset results are presented. This dataset includes 917 images and 7 different…
Purpose: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy. Material and…
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether there is a…
Colorectal cancer (CRC) remains a significant cause of cancer-related mortality, despite the widespread implementation of prophylactic initiatives aimed at detecting and removing precancerous polyps. Although screening effectively reduces…
Significance: Cervical cancer progresses through cervical intraepithelial neoplasia (CIN), which are precursor lesions of cervical cancer. In low-grade CIN, atypical cells generate inside the squamous epithelium, which causes the accuracy…
An important part of breast cancer staging is the assessment of the sentinel axillary node for early signs of tumor spreading. However, this assessment by pathologists is not always easy and retrospective surveys often requalify the status…
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements.…
In this paper we discuss lung cancer detection using hybrid model of Convolutional-Neural-Networks (CNNs) and Support-Vector-Machines-(SVMs) in order to gain early detection of tumors, benign or malignant. The work uses this hybrid model by…
Melanoma diagnosed and treated in its early stages can increase the survival rate. A projected increase in skin cancer incidents and a dearth of dermatopathologists have emphasized the need for computational pathology (CPATH) systems. CPATH…
The computer-aided detection (CADe) systems are developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing missing inspections. Many studies have shown such a CADe system with deep learning approaches…
Breast cancer has the highest incidence and second highest mortality rate for women in the US. Our study aims to utilize deep learning for benign/malignant classification of mammogram tumors using a subset of cases from the Digital Database…
Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell…
Breast cancer is the most common cancer among women worldwide. Early-stage diagnosis of breast cancer can significantly improve the efficiency of treatment. Computer-aided diagnosis (CAD) systems are widely adopted in this issue due to…
This research presents an innovative approach to cancer diagnosis and prediction using explainable Artificial Intelligence (XAI) and deep learning techniques. With cancer causing nearly 10 million deaths globally in 2020, early and accurate…
Breast cancer is one of the most common causes of cancer-related death in women worldwide. Early and accurate diagnosis of breast cancer may significantly increase the survival rate of patients. In this study, we aim to develop a fully…
Convolutional neural networks (CNNs) have shown great promise in improving computer aided detection (CADe). From classifying tumors found via mammography as benign or malignant to automated detection of colorectal polyps in CT colonography,…
Automatic lymph node segmentation is the cornerstone for advances in computer vision tasks for early detection and staging of cancer. Traditional segmentation methods are constrained by manual delineation and variability in operator…
Objective: We develop a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification…
Breast cancer is a heterogeneous disease with different molecular subtypes, clinical behavior, treatment responses as well as survival outcomes. The development of a reliable, accurate, available and inexpensive method to predict the…