Oncology/Hematology

System level network data and models attack cancer drug resistance.

Drug resistance is responsible for >90% of cancer related deaths. Cancer drug resistance is a system level network phenomenon covering the entire cell. Small-scale interactomes and signalling network models of drug resistance guide directed drug development. Recently, proteome-wide human interactome and signalling network data have become available, which have been extended by drug-target interact...

Class-aware multi-level attention learning for semi-supervised breast cancer diagnosis under imbalanced label distribution.

Breast cancer affects a significant number of patients worldwide, and early diagnosis is critical for improving cure rates and prognosis. Deep learning-based breast cancer classification algorithms have substantially alleviated the burden on medical personnel. However, existing breast cancer diagnosis models face notable limitations which are challenging to obtain in clinical settings, such as rel...

You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction.

Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishes the burden of this disease, it has some flaws re...

Breast cancer classification based on hybrid CNN with LSTM model.

Breast cancer (BC) is a global problem, largely due to a shortage of knowledge and early detection. The speed-up process of detection and classificati...

Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existi...

Cross-ViT based benign and malignant classification of pulmonary nodules.

The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are ma...

Breast cancer image classification based on H&E staining using a causal attention graph neural network model.

Breast cancer image classification remains a challenging task due to the high-resolution nature of pathological images and their complex feature distr...

Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.

PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and...

Machine learning-based integration reveals immunological heterogeneity and the clinical potential of T cell receptor (TCR) gene pattern in hepatocellular carcinoma.

The T Cell Receptor (TCR) significantly contributes to tumor immunity, whereas the intricate interplay with the Hepatocellular Carcinoma (HCC) microen...

Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.

BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), wher...

Identifying potential signatures of immune cells in hepatocellular carcinoma using integrative bioinformatics approaches and machine-learning strategies.

Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging ou...

Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning.

To develop a deep learning model using transfer learning for automatic detection and segmentation of neck lymph nodes (LNs) in computed tomography (CT...

Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images.

Colorectal cancer (CRC) is the second popular cancer in females and third in males, with an increased number of cases. Pathology diagnoses complemente...

A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning.

Effective Breast cancer (BC) analysis is crucial for early prognosis, controlling cancer recurrence, timely medical intervention, and determining appr...

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.

Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process ...

IPNet: An Interpretable Network With Progressive Loss for Whole-Stage Colorectal Disease Diagnosis.

Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of obvious early-stage symptoms. Whole-stage colorectal...

Radiomics Analysis of Different Machine Learning Models based on Multiparametric MRI to Identify Benign and Malignant Testicular Lesions.

RATIONALE AND OBJECTIVES: To develop and validate a machine learning-based prediction model for the use of multiparametric magnetic resonance imaging(...

Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning.

The selection of high-performing cell lines is crucial for biopharmaceutical production but is often time-consuming and labor-intensive. We investigat...

Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer.

Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine...

A machine learning driven computationally efficient horse shoe shaped antenna design for internet of medical things.

With bio-medical wearables becoming an essential part of Internet of Medical things (IoMT) for monitoring the health of workers, patients and others i...