Lnpl-mil: Learning from noisy pseudo labels for promoting multiple instance learning in whole slide image
Z Shao, Y Wang, Y Chen, H Bian… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract Gigapixel Whole Slide Images (WSIs) aided patient diagnosis and prognosis
analysis are promising directions in computational pathology. However, limited by …
analysis are promising directions in computational pathology. However, limited by …
Bi-directional weakly supervised knowledge distillation for whole slide image classification
Computer-aided pathology diagnosis based on the classification of Whole Slide Image
(WSI) plays an important role in clinical practice, and it is often formulated as a weakly …
(WSI) plays an important role in clinical practice, and it is often formulated as a weakly …
DT-MIL: deformable transformer for multi-instance learning on histopathological image
Learning informative representations is crucial for classification and prediction tasks on
histopathological images. Due to the huge image size, whole-slide histopathological image …
histopathological images. Due to the huge image size, whole-slide histopathological image …
A foundation model for clinical-grade computational pathology and rare cancers detection
The analysis of histopathology images with artificial intelligence aims to enable clinical
decision support systems and precision medicine. The success of such applications …
decision support systems and precision medicine. The success of such applications …
Vision transformers for computational histopathology
Computational histopathology is focused on the automatic analysis of rich phenotypic
information contained in gigabyte whole slide images, aiming at providing cancer patients …
information contained in gigabyte whole slide images, aiming at providing cancer patients …
Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self …
Histopathological images contain abundant phenotypic information and pathological
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
patterns, which are the gold standards for disease diagnosis and essential for the prediction …
Deep neural architectures for medical image semantic segmentation
MZ Khan, MK Gajendran, Y Lee, MA Khan - IEEE Access, 2021 - ieeexplore.ieee.org
Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …
Deep learning methods for lung cancer segmentation in whole-slide histopathology images—the acdc@ lunghp challenge 2019
Accurate segmentation of lung cancer in pathology slides is a critical step in improving
patient care. We proposed the ACDC@ LungHP (Automatic Cancer Detection and …
patient care. We proposed the ACDC@ LungHP (Automatic Cancer Detection and …
Position-based anchor optimization for point supervised dense nuclei detection
Nuclei detection is one of the most fundamental and challenging problems in
histopathological image analysis, which can localize nuclei to provide effective computer …
histopathological image analysis, which can localize nuclei to provide effective computer …
[HTML][HTML] Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels
Tissue-level semantic segmentation is a vital step in computational pathology. Fully-
supervised models have already achieved outstanding performance with dense pixel-level …
supervised models have already achieved outstanding performance with dense pixel-level …