Related papers: Deep learning generates custom-made logistic regre…

Objective: This paper proposes a deep learning model for breast cancer detection from reconstructed images of microwave imaging scan data and aims to improve the accuracy and efficiency of breast tumor detection, which could have a…

Machine learning (ML) and deep learning (DL) models have been employed to significantly improve analyses of medical imagery, with these approaches used to enhance the accuracy of prediction and classification. Model predictions and…

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…

Deep learning has been reported to achieve high performances in the detection of skin cancer, yet many challenges regarding the reproducibility of results and biases remain. This study is a replication (different data, same analysis) of a…

The rapid advancement of deep learning (DL) has transformed healthcare, particularly in cancer detection and diagnosis. DL surpasses traditional machine learning and human accuracy, making it a critical tool for identifying diseases.…

Objective: The majority of detailed patient information in real-world data (RWD) is only consistently available in free-text clinical documents. Manual curation is expensive and time-consuming. Developing natural language processing (NLP)…

To address the issues of limited samples, time-consuming feature design, and low accuracy in detection and classification of breast cancer pathological images, a breast cancer image classification model algorithm combining deep learning and…

Phyllodes tumors (PTs) are rare fibroepithelial breast lesions that are difficult to classify preoperatively due to their radiological similarity to benign fibroadenomas. This often leads to unnecessary surgical excisions. To address this,…

Metastasis is the leading cause of cancer-related mortality, yet most predictive models rely on shallow architectures and neglect patient-specific regulatory mechanisms. Here, we integrate classical machine learning and deep learning to…

The choice of the most effective treatment may eventually be influenced by breast cancer survival prediction. To predict the chances of a patient surviving, a variety of techniques were employed, such as statistical, machine learning, and…

The integration of multi-modal data, such as pathological images and genomic data, is essential for understanding cancer heterogeneity and complexity for personalized treatments, as well as for enhancing survival predictions. Despite the…

We present a deep transformation model for probabilistic regression. Deep learning is known for outstandingly accurate predictions on complex data but in regression tasks, it is predominantly used to just predict a single number. This…

Breast cancer treatment still remains a challenge, where molecular subtypes classification plays a crucial role in selecting appropriate and specific therapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype, and…

Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Immunohistochemistry (IHC) biomarkers like HER2, ER, and PR are critical for identifying breast cancer subtypes.…

Breast cancer has become one of the most prevalent cancers by which people all over the world are affected and is posed serious threats to human beings, in a particular woman. In order to provide effective treatment or prevention of this…

In this proposed work, we identified the significant research issues on lung cancer risk factors. Capturing and defining symptoms at an early stage is one of the most difficult phases for patients. Based on the history of patients records,…

In this work, we present a deep learning framework for multi-class breast cancer image classification as our submission to the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer…

Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are…

The use of deep learning (DL) in medical image analysis has significantly improved the ability to predict lung cancer. In this study, we introduce a novel deep convolutional neural network (CNN) model, named ResNet+, which is based on the…

Deep learning methods, in particular convolutional neural networks, have emerged as a powerful tool in medical image computing tasks. While these complex models provide excellent performance, their black-box nature may hinder real-world…