Oncology/Hematology

CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network.

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequen...

A Machine Learning Tool Using Digital Microscopy (Morphogo) for the Identification of Abnormal Lymphocytes in the Bone Marrow.

Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver variability. The morphological differential diagnosis of abnormal lymphocytes from normal lymphocytes is still challenging. The digital pathology method...

MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule.

This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and...

Colorectal cancer surgery: by Cambridge Medical Robotics Versius Surgical Robot System-a single-institution study. Our experience.

With the previous experiences in performing laparoscopic for over a period of 15 years and da Vinci colorectal surgeries from 2010 to 2013, we started...

Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.

The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the progno...

Accurate prediction of breast cancer survival through coherent voting networks with gene expression profiling.

For a patient affected by breast cancer, after tumor removal, it is necessary to decide which adjuvant therapy is able to prevent tumor relapse and fo...

Performance Comparisons of AlexNet and GoogLeNet in Cell Growth Inhibition IC50 Prediction.

Drug responses in cancer are diverse due to heterogenous genomic profiles. Drug responsiveness prediction is important in clinical response to specifi...

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images.

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing ...

Skin Cancer Detection Using Kernel Fuzzy C-Means and Improved Neural Network Optimization Algorithm.

Early diagnosis of malignant skin cancer from images is a significant part of the cancer treatment process. One of the principal purposes of this rese...

Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation.

BACKGROUND: Manual delineation of gross tumor volume (GTV) is essential for radiotherapy treatment planning, but it is time-consuming and suffers inte...

A fuzzy rank-based ensemble of CNN models for classification of cervical cytology.

Cervical cancer affects more than 0.5 million women annually causing more than 0.3 million deaths. Detection of cancer in its early stages is of prime...

Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade.

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictabili...

An Evolutionary Approach for the Enhancement of Dermatological Images and Their Classification Using Deep Learning Models.

Dermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to...

Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification.

Convolutional neural networks (CNNs) are state-of-the-art computer vision techniques for various tasks, particularly for image classification. However...

Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma.

The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival ...

Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases.

Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities...

Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network.

Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personali...

Combining Genetic Algorithms and SVM for Breast Cancer Diagnosis Using Infrared Thermography.

Breast cancer is one of the leading causes of mortality globally, but early diagnosis and treatment can increase the cancer survival rate. In this con...

Deep Learning on MRI Images for Diagnosis of Lung Cancer Spinal Bone Metastasis.

This paper aimed to explore the adoption of deep learning algorithms in lung cancer spinal bone metastasis diagnosis. Comprehensive analysis was carri...

Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy.

BACKGROUND: Accurate assessment of the axillary lymph nodes (aLNs) in breast cancer patients is essential for prognosis and treatment planning. Curren...