Oncology/Hematology

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painf...

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.

To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed an artificial intelligence (AI)-augmented smart pathology review system (SmartPath) to empower pathologists with quantitative metrics for accurately ...

Robotic vs. laparoscopic intersphincteric resection for low rectal cancer: a case matched study reporting a median of 7-year long-term oncological and functional outcomes.

Aim of this study was to compare operative, long-term oncological and functional outcomes of laparoscopic (LISR) and robotic (RISR) intersphincteric r...

Development of machine learning support for reading whole body diffusion-weighted MRI (WB-MRI) in myeloma for the detection and quantification of the extent of disease before and after treatment (MALIMAR): protocol for a cross-sectional diagnostic test accuracy study.

INTRODUCTION: Whole-body MRI (WB-MRI) is recommended by the National Institute of Clinical Excellence as the first-line imaging tool for diagnosis of ...

An Improved Neural Network Based on SENet for Sleep Stage Classification.

Sleep staging is an important step in analyzing sleep quality. Traditional manual analysis by psychologists is time-consuming. In this paper, we propo...

A Fully Deep Learning Paradigm for Pneumoconiosis Staging on Chest Radiographs.

Pneumoconiosis staging has been a very challenging task, both for certified radiologists and computer-aided detection algorithms. Although deep learni...

Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.

OBJECTIVES: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of thi...

Deep learning-based automated quantification of goblet cell mucus using histological images as a predictor of clinical relapse of ulcerative colitis with endoscopic remission.

BACKGROUND: Mucin depletion is one of the histological indicators of clinical relapse among patients with ulcerative colitis (UC). Mucin depletion is ...

MLRD-Net: 3D multiscale local cross-channel residual denoising network for MRI-based brain tumor segmentation.

The precise segmentation of multimodal MRI images is the primary stage of tumor diagnosis and treatment. Current segmentation strategies often underut...

FBCU-Net: A fine-grained context modeling network using boundary semantic features for medical image segmentation.

The performance of deep learning-based medical image segmentation methods largely depends on the segmentation accuracy of tissue boundaries. However, ...

Computer-Vision-Based Dielectrophoresis Mobility Tracking for Characterization of Single-Cell Biophysical Properties.

Fast and precise measurements of live single-cell biophysical properties is significant in disease diagnosis, cytopathologic analysis, etc. Existing m...

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra.

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes cruc...

Artificial intelligence in pancreatic cancer.

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early sc...

Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool.

In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non-linear controllers. The re...

Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM.

OBJECTIVE: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of sur...

Implementation of machine learning in the clinic: challenges and lessons in prospective deployment from the System for High Intensity EvaLuation During Radiation Therapy (SHIELD-RT) randomized controlled study.

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) have resulted in significant enthusiasm for their promise in healthcare. Despite th...

Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning.

Cancer prognosis analysis is of essential interest in clinical practice. In order to explore the prognostic power of computational histopathology and ...

Deep Learning Approaches for Detection of Breast Adenocarcinoma Causing Carcinogenic Mutations.

Genes are composed of DNA and each gene has a specific sequence. Recombination or replication within the gene base ends in a permanent change in the n...

Explainable artificial intelligence for precision medicine in acute myeloid leukemia.

Artificial intelligence (AI) can unveil novel personalized treatments based on drug screening and whole-exome sequencing experiments (WES). However, t...

Development and validation of a deep learning signature for predicting lymph node metastasis in lung adenocarcinoma: comparison with radiomics signature and clinical-semantic model.

OBJECTIVE: To develop and validate a deep learning (DL) signature for predicting lymph node (LN) metastasis in patients with lung adenocarcinoma.