Related papers: Towards Comprehensive Cellular Characterisation of…
Current pathology workflow involves staining of thin tissue slices, which otherwise would be transparent, followed by manual investigation under the microscope by a trained pathologist. While the hematoxylin and eosin (H&E) stain is…
Breast cancer is one of the leading causes of death for women worldwide. Early screening is essential for early identification, but the chance of survival declines as the cancer progresses into advanced stages. For this study, the most…
Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL). However, given the paucity of labeled histology data, direct application of MIL can easily…
Breast Cancer is the most prevalent cancer in the world. The World Health Organization reports that the disease still affects a significant portion of the developing world citing increased mortality rates in the majority of low to middle…
Large amounts of unlabelled data are commonplace for many applications in computational pathology, whereas labelled data is often expensive, both in time and cost, to acquire. We investigate the performance of unsupervised and supervised…
Early detection and prognosis of breast cancer are feasible by utilizing histopathological grading of biopsy specimens. This research is focused on detection and grading of nuclear pleomorphism in histopathological images of breast cancer.…
Efficient and precise quantification of lymphocytes in histopathology slides is imperative for the characterization of the tumor microenvironment and immunotherapy response insights. We developed a data-centric optimization pipeline that…
Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular details to form comprehensive diagnoses. However, existing…
Deep learning models show promise in digital pathology, but their opaque decision-making processes limit trust and clinical adoption. To address this challenge, we present HIPPO, an explainable AI method for analyzing weakly-supervised…
With the advanced imaging technology, digital pathology imaging of tumor tissue slides is becoming a routine clinical procedure for cancer diagnosis. This process produces massive imaging data that capture histological details in high…
MET is a proto-oncogene whose somatic activation in non-small cell lung cancer leads to increased cell growth and tumor progression. The two major classes of MET alterations are gene amplification and exon 14 deletion, both of which are…
Digital pathology enables automatic analysis of histopathological sections using artificial intelligence (AI). Automatic evaluation could improve diagnostic efficiency and help find associations between morphological features and clinical…
Accurate classification of pediatric central nervous system tumors remains challenging due to histological complexity and limited training data. While pathology foundation models have advanced whole-slide image (WSI) analysis, they often…
With the advent of novel cancer treatment options such as immunotherapy, studying the tumour immune micro-environment (TIME) is crucial to inform on prognosis and understand potential response to therapeutic agents. A key approach to…
Masked image modelling (MIM) is a powerful self-supervised representation learning paradigm, whose potential has not been widely demonstrated in medical image analysis. In this work, we show the capacity of MIM to capture rich semantic…
The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly…
While microscopic analysis of histopathological slides is generally considered as the gold standard method for performing cancer diagnosis and grading, the current method for analysis is extremely time consuming and labour intensive as it…
Large-scale image data such as digital whole-slide histology images pose a challenging task at annotation software solutions. Today, a number of good solutions with varying scopes exist. For cell annotation, however, we find that many do…
Spatial Transcriptomics (ST) allows a high-resolution measurement of RNA sequence abundance by systematically connecting cell morphology depicted in Hematoxylin and Eosin (H&E) stained histology images to spatially resolved gene…
Colon and Lung cancer is one of the most perilous and dangerous ailments that individuals are enduring worldwide and has become a general medical problem. To lessen the risk of death, a legitimate and early finding is particularly required.…