Vision Transformers in medical computer vision—A contemplative retrospection
Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …
contained within images, have evolved as one of the most contemporary and dominant …
Artificial intelligence for digital and computational pathology
Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …
including deep learning, have boosted the field of computational pathology. This field holds …
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 …
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 …
[HTML][HTML] One model is all you need: multi-task learning enables simultaneous histology image segmentation and classification
The recent surge in performance for image analysis of digitised pathology slides can largely
be attributed to the advances in deep learning. Deep models can be used to initially localise …
be attributed to the advances in deep learning. Deep models can be used to initially localise …
A survey on artificial intelligence in histopathology image analysis
The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …
dramatically transformed pathologists' workflow and allowed the use of computer systems in …
Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: a systematic review
This systematic review analyses and describes the application and diagnostic accuracy of
Artificial Intelligence (AI) methods used for detection and grading of potentially malignant …
Artificial Intelligence (AI) methods used for detection and grading of potentially malignant …
MCUa: Multi-level context and uncertainty aware dynamic deep ensemble for breast cancer histology image classification
Breast histology image classification is a crucial step in the early diagnosis of breast cancer.
In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have …
In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have …
An enhanced histopathology analysis: An ai-based system for multiclass grading of oral squamous cell carcinoma and segmenting of epithelial and stromal tissue
Simple Summary An established dataset of histopathology images obtained by biopsy and
reviewed by two pathologists is used to create a two-stage oral squamous cell carcinoma …
reviewed by two pathologists is used to create a two-stage oral squamous cell carcinoma …
Multiple instance captioning: Learning representations from histopathology textbooks and articles
We present ARCH, a computational pathology (CP) multiple instance captioning dataset to
facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH …
facilitate dense supervision of CP tasks. Existing CP datasets focus on narrow tasks; ARCH …