Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification
Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is
broadly used in diagnostic pathology laboratories for patient care. So far, however, clinical …
broadly used in diagnostic pathology laboratories for patient care. So far, however, clinical …
Multi-scale hypergraph-based feature alignment network for cell localization
Cell localization in medical image analysis is a challenging task due to the significant
variation in cell shape, size and color. Existing localization methods continue to tackle these …
variation in cell shape, size and color. Existing localization methods continue to tackle these …
Exponential distance transform maps for cell localization
Cell localization in medical image analysis aims for precise identification of cell positions.
Existing methods involve predicting density maps from images, followed by post-processing …
Existing methods involve predicting density maps from images, followed by post-processing …
Lite-UNet: A lightweight and efficient network for cell localization
Cell localization constitutes a fundamental research domain within the realm of pathology
image analysis, with its core objective being the precise identification of cell spatial …
image analysis, with its core objective being the precise identification of cell spatial …
Learning to count biological structures with raters' uncertainty
Exploiting well-labeled training sets has led deep learning models to astonishing results for
counting biological structures in microscopy images. However, dealing with weak multi-rater …
counting biological structures in microscopy images. However, dealing with weak multi-rater …
Learning with limited target data to detect cells in cross-modality images
Deep neural networks have achieved excellent cell or nucleus quantification performance in
microscopy images, but they often suffer from performance degradation when applied to …
microscopy images, but they often suffer from performance degradation when applied to …
Semantic Generative Augmentations for Few-Shot Counting
P Doubinsky, N Audebert… - Proceedings of the …, 2024 - openaccess.thecvf.com
With the availability of powerful text-to-image diffusion models, recent works have explored
the use of synthetic data to improve image classification performances. These works show …
the use of synthetic data to improve image classification performances. These works show …
Feature masking on non-overlapping regions for detecting dense cells in blood smear image
Detecting cells in blood smear images is of great significance for automatic diagnosis of
blood diseases. However, this task is rather challenging, mainly because there are dense …
blood diseases. However, this task is rather challenging, mainly because there are dense …
Difference-deformable convolution with pseudo scale instance map for cell localization
Cell localization still faces two unresolved challenges: 1) the dramatic variations in cell
morphology, coupled with the heterogeneous intensity distribution of lightly stained cells; 2) …
morphology, coupled with the heterogeneous intensity distribution of lightly stained cells; 2) …