Oncology/Hematology

An Instant Tumor Detection Method for Ex-vivo Tissue Palpation Utilizing Self-learning High-density Flexible Tactile Sensor Array Based on Attention Mechanism Neural Network.

This paper reports an innovative and instant method for tumor detection. We delineate the tactile signal matrixes of tumor tissue with varying stiffness utilizing self-learning high-density flexible tactile sensor array, and depict the morphological features of the tumor at the fingertip tactile perception with an attention mechanism neural network. A novel algorithm to instantaneously recognize t...

A comparison between Deep Learning architectures for the assessment of breast tumor segmentation using VSI ultrasound protocol.

Automatic breast tumor ultrasound segmentation is one of the most critical components in the development of tools for breast cancer diagnosis. Several deep learning algorithms have been tested with public and private datasets but none of them has been designed for asynchronous protocol ultrasound acquisition. In this work, a dataset collected through the Volume Sweep Imaging protocol for breast ul...

High-rate emphasized DeepLabV3Plus for Semantic Segmentation of Breast Cancer-related Hematoxylin and Eosin-stained Images.

Deep learning algorithms have been successfully adopted to extract meaningful information from digital images, yet many of them have been untapped in ...

Beyond Dysplasia: Uncovering Structure in Oral Potentially Malignant Diseases with Unsupervised Contrastive Learning.

Automated cancer diagnosis research often focuses on a binary task - recognize dysplasia and cancer from other lesions. However, other clinical condit...

Stiffness analysis of meningiomas using neural network-based inversion on MR Elastography.

Meningiomas are the most prevalent benign intracranial tumors, and surgical intervention is the primary treatment. The physical characteristics of men...

C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading.

Graph-based learning approaches, due to their ability to encode tissue/organ structure information, are increasingly favored for grading colorectal ca...

Evolutionary learning-derived lncRNA signature with biomarker discovery for predicting stage of colon adenocarcinoma.

In recent years, long non-coding RNAs (lncRNAs) have emerged as potential regulators of biological processes and genes, with the potential to serve as...

The expanding role of artificial intelligence in the histopathological diagnosis in urological oncology: a literature review.

The ongoing growth of artificial intelligence (AI) involves virtually every aspect of oncologic care in medicine. Although AI is in its infancy, it ha...

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations a...

CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell ...

Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers

Acute Leukemia is the most common hematologic malignancy in children and adolescents. A key methodology in the diagnostic evaluation of this maligna...

Concordance in basal cell carcinoma diagnosis. Building a proper ground truth to train Artificial Intelligence tools

Background: The existence of different basal cell carcinoma (BCC) clinical criteria cannot be objectively validated. An adequate ground-truth is nee...

Artificial intelligence for automatic detection of basal cell carcinoma from frozen tissue tangential biopsies.

Evaluation of basal cell carcinoma (BCC) involves tangential biopsies of a suspicious lesion that is sent for frozen sections and evaluated by a Mohs ...

Artificial intelligence-empowered cellular morphometric risk score improves prognostic stratification of cutaneous squamous cell carcinoma.

BACKGROUND: Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients.

[Application value of artificial intelligence in surgical precision diagnosis and treatment of rectal cancer].

Colorectal cancer is the most common malignant tumor of digestive tract, and the incidence of colorectal cancer in China is especially characterized b...

Study of prognostic splicing factors in cancer using machine learning approaches.

Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRN...

Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion

Considering the increased workload in pathology laboratories today, automated tools such as artificial intelligence models can help pathologists wit...

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to ...

Skin Cancer Images Classification using Transfer Learning Techniques

Skin cancer is one of the most common and deadliest types of cancer. Early diagnosis of skin cancer at a benign stage is critical to reducing cancer...

Location-based Radiology Report-Guided Semi-supervised Learning for Prostate Cancer Detection

Prostate cancer is one of the most prevalent malignancies in the world. While deep learning has potential to further improve computer-aided prostate...