Oncology/Hematology

Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology.

BACKGROUND: We aimed to develop an artificial intelligence (AI)-assisted oral cytology method, similar to cervical cytology. We focused on the detection of cell nuclei because the ratio of cell nuclei to cytoplasm increases with increasing cell malignancy. As an initial step in the development of AI-assisted cytology, we investigated two methods for the automatic detection of cell nuclei in blue-s...

Clavien-Dindo classification and risk prediction model of complications after robot-assisted radical hysterectomy for cervical cancer.

Although significant progress has been made with surgical methods, the incidence of complications after minimally invasive surgery in patients with cervical cancer remains high. Established as a standardized system, Clavien-Dindo classification (CDC) has been applied in a variety of surgical fields. This study is designed to evaluate the complications after robot-assisted radical hysterectomy (RRH...

The involvement of gut microbiota in the anti-tumor effect of carnosic acid via IL-17 suppression in colorectal cancer.

Colorectal cancer (CRC) is a malignant tumor that threatens human health worldwide. Disturbance of the gut microbiota caused by various external facto...

Ovarian Cancer-Self Assessment: An Innovation for Early Detection and Risk Assessment of Ovarian Cancer.

OBJECTIVE: The modality to detect ovarian cancer at an early stage is very limited. Early diagnosis determines the prognosis. This study aimed to deve...

Artificial intelligence predicts lymph node metastasis or risk of lymph node metastasis in T1 colorectal cancer.

BACKGROUND: The treatment strategies for colorectal cancer (CRC) must ensure a radical cure of cancer and prevent over/under treatment. Biopsy specime...

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs.

AIMS: Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose di...

Transformer-based unsupervised contrastive learning for histopathological image classification.

A large-scale and well-annotated dataset is a key factor for the success of deep learning in medical image analysis. However, assembling such large an...

A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.

BACKGROUND In this study we aimed to establish a new transfer learning model based on noncontrast and thin-layer computed tomography (CT) scans to dis...

Deep Learning-Based Image Analysis for the Quantification of Tumor-Induced Angiogenesis in the 3D In Vivo Tumor Model-Establishment and Addition to Laser Speckle Contrast Imaging (LSCI).

(1) Background: angiogenesis plays an important role in the growth and metastasis of tumors. We established the CAM assay application, an image analys...

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images.

Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have bec...

The Use of Deep Learning-Based Computer Diagnostic Algorithm for Detection of Lymph Node Metastases of Gastric Adenocarcinoma.

The diversifying modalities of treatment for gastric cancer raise urgent demands for the rapid and precise diagnosis of metastases in regional lymph ...

Automated histological classification for digital pathology images of colonoscopy specimen via deep learning.

Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis. This study aimed to develop and validat...

Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.

Cancer of the mesothelium, sometimes referred to as malignant mesothelioma (MM), is an extremely uncommon form of the illness that almost always resul...

An Efficient Multi-Scale Convolutional Neural Network Based Multi-Class Brain MRI Classification for SaMD.

A brain tumor is the growth of abnormal cells in certain brain tissues with a high mortality rate; therefore, it requires high precision in diagnosis,...

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The...

Surgical and functional outcomes of robot-assisted laparoscopic partial nephrectomy for renal cell carcinoma in adolescents and young adults: a propensity score matching study.

BACKGROUND: Cancer development in adolescents and young adults (AYAs) has elicited recent interest. We investigated the surgical and functional outcom...

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center.

BACKGROUND: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer va...

BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images.

Acute lymphoblastic leukemia (ALL) is a deadly cancer characterized by aberrant accumulation of immature lymphocytes in the blood or bone marrow. Effe...

Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology.

BACKGROUND: Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, ...

Development and validation of a meta-learning-based multi-modal deep learning algorithm for detection of peritoneal metastasis.

PURPOSE: The existing medical imaging tools have a detection accuracy of 97% for peritoneal metastasis(PM) bigger than 0.5 cm, but only 29% for that s...