Cluster-based histopathology phenotype representation learning by self-supervised multi-class-token hierarchical ViT.

Developing a clinical AI model necessitates a significant amount of highly curated and carefully annotated dataset by multiple medical experts, which results in increased development time and costs. Self-supervised learning (SSL) is a method that enables AI models to leverage unlabelled data to acquire domain-specific background knowledge that can enhance their performance on various downstream tasks. In this work, we introduce CypherViT, a cluster-based histo-pathology phenotype representation learning by self-supervised multi-class-token hierarchical Vision Transformer (ViT).

Computer-aided scoring of () gene amplification status in breast cancer.

Background: Identification of HER2 protein overexpression and/or amplification of the gene are required to qualify breast cancer patients for HER2 targeted therapies. hybridization (ISH) assays that identify gene amplification function as a stand-alone test for determination of HER2 status and rely on the manual quantification of the number of genes and copies of chromosome 17 to determine amplification.

Methods: To assist pathologists, we have developed the uPath HER2 Dual ISH Image Analysis for Breast (uPath HER2 DISH IA) algorithm, as an adjunctive aid in the determination of gene status in breast cancer specimens.

A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes.

Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes.

Multimodal registration of SD-OCT volumes and fundus photographs using histograms of oriented gradients.

With availability of different retinal imaging modalities such as fundus photography and spectral domain optical coherence tomography (SD-OCT), having a robust and accurate registration scheme to enable utilization of this complementary information is beneficial. The few existing fundus-OCT registration approaches contain a vessel segmentation step, as the retinal blood vessels are the most dominant structures that are in common between the pair of images. However, errors in the vessel segmentation from either modality may cause corresponding errors in the registration.

Incorporation of gradient vector flow field in a multimodal graph-theoretic approach for segmenting the internal limiting membrane from glaucomatous optic nerve head-centered SD-OCT volumes.

The internal limiting membrane (ILM) separates the retina and optic nerve head (ONH) from the vitreous. In the optical coherence tomography volumes of glaucoma patients, while current approaches for the segmentation of the ILM in the peripapillary and macular regions are considered robust, current approaches commonly produce ILM segmentation errors at the ONH due to the presence of blood vessels and/or characteristic glaucomatous deep cupping. Because a precise segmentation of the ILM surface at the ONH is required for computing several newer structural measurements including Bruch's membrane opening-minimum rim width (BMO-MRW) and cup volume, in this study, we propose a multimodal multiresolution graph-based method to precisely segment the ILM surface within ONH-centered spectral-domain optical coherence tomography (SD-OCT) volumes.

Multimodal Segmentation of Optic Disc and Cup From SD-OCT and Color Fundus Photographs Using a Machine-Learning Graph-Based Approach.

In this work, a multimodal approach is proposed to use the complementary information from fundus photographs and spectral domain optical coherence tomography (SD-OCT) volumes in order to segment the optic disc and cup boundaries. The problem is formulated as an optimization problem where the optimal solution is obtained using a machine-learning theoretical graph-based method. In particular, first the fundus photograph is registered to the 2D projection of the SD-OCT volume.

Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction.

Retinal images can be used in several applications, such as ocular fundus operations as well as human recognition. Also, they play important roles in detection of some diseases in early stages, such as diabetes, which can be performed by comparison of the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel detection process difficult.