Pathology

From modern CNNs to vision transformers: Assessing the performance, robustness, and classification strategies of deep learning models in histopathology.

While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere classification accuracy. In order to fill this gap, we developed a new methodology to extensively evaluate a wide range of classification models, including recent vision transform...

Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma.

BACKGROUND AND PURPOSE: An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoencoder-based pattern analysis could provide interpretable tissue labeling and prognostic value in isocitrate dehydrogenase () wild-type glioblastoma.

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology.

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine...

Bioinspired, ingestible electroceutical capsules for hunger-regulating hormone modulation.

The gut-brain axis, which is mediated via enteric and central neurohormonal signaling, is known to regulate a broad set of physiological functions fro...

Contribution of whole slide imaging-based deep learning in the assessment of intraoperative and postoperative sections in neuropathology.

The pathological diagnosis of intracranial germinoma (IG), oligodendroglioma, and low-grade astrocytoma on intraoperative frozen section (IFS) and hem...

Adrenal lesion classification with abdomen caps and the effect of ROI size.

Accurate classification of adrenal lesions on magnetic resonance (MR) images are very important for diagnosis and treatment planning. The detection an...

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tiss...

Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning.

Gene mutation detection is usually carried out by molecular biological methods, which is expensive and has a long-time cycle. In contrast, pathologica...

3D Visualization, Skeletonization and Branching Analysis of Blood Vessels in Angiogenesis.

Angiogenesis is the process of new blood vessels growing from existing vasculature. Visualizing them as a three-dimensional (3D) model is a challengin...

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.

BACKGROUND: Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lo...

Artificial Intelligence in Point-of-Care Testing.

With the projected increase in the global population, current healthcare delivery models will face severe challenges. Rural and remote areas, whether ...

Diagnosing the benign paroxysmal positional vertigo via 1D and deep-learning composite model.

BACKGROUND: Benign Paroxysmal Positional Vertigo (BPPV) is the leading cause of vertigo, and its characteristic nystagmus induced by positional maneuv...

Distribution based MIL pooling filters: Experiments on a lymph node metastases dataset.

Histopathology is a crucial diagnostic tool in cancer and involves the analysis of gigapixel slides. Multiple instance learning (MIL) promises success...

Classification of pulmonary sounds through deep learning for the diagnosis of interstitial lung diseases secondary to connective tissue diseases.

Early diagnosis of interstitial lung diseases secondary to connective tissue diseases is critical for the treatment and survival of patients. The symp...

Use of a deep learning algorithm for non-mass enhancement on breast MRI: comparison with radiologists' interpretations at various levels.

PURPOSE: To evaluate the diagnostic performance of deep learning using the Residual Networks 50 (ResNet50) neural network constructed from different s...

Deep learning-based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma.

AIMS: Classification of histological patterns in lung adenocarcinoma (LUAD) is critical for clinical decision-making, especially in the early stage. H...

Standardizing analysis of intra-tumoral heterogeneity with computational pathology.

Many malignant cancers like glioblastoma are highly adaptive diseases that dynamically change their regional biology to survive and thrive under diver...

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study.

OBJECTIVES: To develop deep learning-assisted diagnosis models based on CT images to facilitate radiologists in differentiating benign and malignant p...

Digital Histopathology by Infrared Spectroscopic Imaging.

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical mak...

Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images.

Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. ...