Deep learning in histopathology: the path to the clinic
Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …
Multi-modal 3d object detection in autonomous driving: A survey and taxonomy
Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …
Segment anything model for medical images?
Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …
segmentation. It has achieved impressive results on various natural image segmentation …
Transformer-based unsupervised contrastive learning for histopathological image classification
A large-scale and well-annotated dataset is a key factor for the success of deep learning in
medical image analysis. However, assembling such large annotations is very challenging …
medical image analysis. However, assembling such large annotations is very challenging …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
[HTML][HTML] The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
Recently, deep learning frameworks have rapidly become the main methodology for
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
analyzing medical images. Due to their powerful learning ability and advantages in dealing …
Lizard: A large-scale dataset for colonic nuclear instance segmentation and classification
S Graham, M Jahanifar, A Azam… - Proceedings of the …, 2021 - openaccess.thecvf.com
The development of deep segmentation models for computational pathology (CPath) can
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …
help foster the investigation of interpretable morphological biomarkers. Yet, there is a major …
A comprehensive review of deep learning in colon cancer
Deep learning has emerged as a leading machine learning tool in object detection and has
attracted attention with its achievements in progressing medical image analysis …
attracted attention with its achievements in progressing medical image analysis …
Pseudo-label guided contrastive learning for semi-supervised medical image segmentation
Although recent works in semi-supervised learning (SemiSL) have accomplished significant
success in natural image segmentation, the task of learning discriminative representations …
success in natural image segmentation, the task of learning discriminative representations …
Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology
JM Dolezal, A Srisuwananukorn, D Karpeyev… - Nature …, 2022 - nature.com
A model's ability to express its own predictive uncertainty is an essential attribute for
maintaining clinical user confidence as computational biomarkers are deployed into real …
maintaining clinical user confidence as computational biomarkers are deployed into real …