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Found 11 papers, 4 papers with code
StainFuser: Controlling Diffusion for Faster Neural Style Transfer in Multi-Gigapixel Histology Images
Stain normalization algorithms aim to transform the color and intensity characteristics of a source multi-gigapixel histology image to match those of a target image, mitigating inconsistencies in the appearance of stains used to highlight cellular components in the images.
Scene Graph Aided Radiology Report Generation
Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports.
Can Prompt Learning Benefit Radiology Report Generation?
Radiology report generation aims to automatically provide clinically meaningful descriptions of radiology images such as MRI and X-ray.
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists.
Nuclear Segmentation and Classification: On Color & Compression Generalization
Since the introduction of digital and computational pathology as a field, one of the major problems in the clinical application of algorithms has been the struggle to generalize well to examples outside the distribution of the training data.
CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation
Radiology report generation (RRG) has gained increasing research attention because of its huge potential to mitigate medical resource shortages and aid the process of disease decision making by radiologists.
Lane Change Classification and Prediction with Action Recognition Networks
Previous works often adopt physical variables such as driving speed, acceleration and so forth for lane change classification.
Cross-modal Prototype Driven Network for Radiology Report Generation
Radiology report generation (RRG) aims to describe automatically a radiology image with human-like language and could potentially support the work of radiologists, reducing the burden of manual reporting.
A QuadTree Image Representation for Computational Pathology
The field of computational pathology presents many challenges for computer vision algorithms due to the sheer size of pathology images.
Semantic annotation for computational pathology: Multidisciplinary experience and best practice recommendations
The analyses reported in this work highlight best practice recommendations that can be used as annotation guidelines over the lifecycle of a CPath project.
Her2 Challenge Contest: A Detailed Assessment of Automated Her2 Scoring Algorithms in Whole Slide Images of Breast Cancer Tissues
In this paper, we report on a recent automated Her2 scoring contest, held in conjunction with the annual PathSoc meeting held in Nottingham in June 2016, aimed at systematically comparing and advancing the state-of-the-art Artificial Intelligence (AI) based automated methods for Her2 scoring.
