AIMC Topic: Image Interpretation, Computer-Assisted

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Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction.

JCO clinical cancer informatics
PURPOSE: Emerging evidence suggests that the use of artificial intelligence can assist in the timely detection and optimization of therapeutic approach in patients with prostate cancer. The conventional perspective on radiomics encompassing segmentat...

A novel Skin lesion prediction and classification technique: ViT-GradCAM.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinica...

Deep Learning-based Unsupervised Domain Adaptation via a Unified Model for Prostate Lesion Detection Using Multisite Biparametric MRI Datasets.

Radiology. Artificial intelligence
Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materi...

Deep Learning Segmentation of Infiltrative and Enhancing Cellular Tumor at Pre- and Posttreatment Multishell Diffusion MRI of Glioblastoma.

Radiology. Artificial intelligence
Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free su...

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Radiology. Artificial intelligence
Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI withou...

Predicting Overall Survival of Glioblastoma Patients Using Deep Learning Classification Based on MRIs.

Studies in health technology and informatics
INTRODUCTION: Glioblastoma (GB) is one of the most aggressive tumors of the brain. Despite intensive treatment, the average overall survival (OS) is 15-18 months. Therefore, it is helpful to be able to assess a patient's OS to tailor treatment more s...

Automatic Classification of Wound Images Showing Healing Complications: Towards an Optimised Approach for Detecting Maceration.

Studies in health technology and informatics
This study aims to advance the field of digital wound care by developing and evaluating convolutional neural network (CNN) architectures for the automatic classification of maceration, a significant wound healing complication, in 458 annotated wound ...

Explaining Deep Learning Models Applied in Histopathology: Current Developments and the Path to Sustainability.

Studies in health technology and informatics
The digital pathology landscape is in continuous expansion. The digitalization of slides using WSIs (Whole Slide Images) fueled the capacity of automatic support for diagnostics. The paper presents an overview of the current state of the art methods ...

Automatic Tumor Cellularity Measurement: AI-Based Pipeline for Multi-Organ Pathology Imaging.

Studies in health technology and informatics
Tumor Cellularity (TC) is an important metric for assessing organ tumor burden. However, manual cell counting is not feasible due to large volumes of pathology images and inconsistent measurements between pathologists. The PAIP 2023 Challenge aimed t...