Dual-curriculum contrastive multi-instance learning for cancer prognosis analysis with whole slide images
The multi-instance learning (MIL) has advanced cancer prognosis analysis with whole slide
images (WSIs). However, current MIL methods for WSI analysis still confront unique …
images (WSIs). However, current MIL methods for WSI analysis still confront unique …
Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification
Abstract Multi-Instance Learning (MIL) has shown impressive performance for
histopathology whole slide image (WSI) analysis using bags or pseudo-bags. It involves …
histopathology whole slide image (WSI) analysis using bags or pseudo-bags. It involves …
[HTML][HTML] Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images
Multiple instance learning (MIL) models have achieved remarkable success in analyzing
whole slide images (WSIs) for disease classification problems. However, with regard to giga …
whole slide images (WSIs) for disease classification problems. However, with regard to giga …
Lnpl-mil: Learning from noisy pseudo labels for promoting multiple instance learning in whole slide image
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 …
Multiple instance learning with center embeddings for histopathology classification
P Chikontwe, M Kim, SJ Nam, H Go… - Medical Image Computing …, 2020 - Springer
Histopathology image analysis plays an important role in the treatment and diagnosis of
cancer. However, analysis of whole slide images (WSI) with deep learning is challenging …
cancer. However, analysis of whole slide images (WSI) with deep learning is challenging …
Prompt-mil: Boosting multi-instance learning schemes via task-specific prompt tuning
Whole slide image (WSI) classification is a critical task in computational pathology, requiring
the processing of gigapixel-sized images, which is challenging for current deep-learning …
the processing of gigapixel-sized images, which is challenging for current deep-learning …
Camil: Causal multiple instance learning for whole slide image classification
Whole slide image (WSI) classification is a crucial component in automated pathology
analysis. Due to the inherent challenges of high-resolution WSIs and the absence of patch …
analysis. Due to the inherent challenges of high-resolution WSIs and the absence of patch …
CAMIL: Context-aware multiple instance learning for cancer detection and subtyping in whole slide images
The visual examination of tissue biopsy sections is fundamental for cancer diagnosis, with
pathologists analyzing sections at multiple magnifications to discern tumor cells and their …
pathologists analyzing sections at multiple magnifications to discern tumor cells and their …
Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole-Slide Image Classification
In computational pathology, whole-slide image (WSI) classification presents a formidable
challenge due to its gigapixel resolution and limited fine-grained annotations. Multiple …
challenge due to its gigapixel resolution and limited fine-grained annotations. Multiple …
Iib-mil: Integrated instance-level and bag-level multiple instances learning with label disambiguation for pathological image analysis
Digital pathology plays a pivotal role in the diagnosis and interpretation of diseases and has
drawn increasing attention in modern healthcare. Due to the huge gigapixel-level size and …
drawn increasing attention in modern healthcare. Due to the huge gigapixel-level size and …