Weakly supervised machine learning
Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …
possible between the training data and outputs, where each training data will predict as a …
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 …
Visual language pretrained multiple instance zero-shot transfer for histopathology images
Contrastive visual language pretraining has emerged as a powerful method for either
training new language-aware image encoders or augmenting existing pretrained models …
training new language-aware image encoders or augmenting existing pretrained models …
Interventional bag multi-instance learning on whole-slide pathological images
Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …
(WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL …
Task-specific fine-tuning via variational information bottleneck for weakly-supervised pathology whole slide image classification
Abstract While Multiple Instance Learning (MIL) has shown promising results in digital
Pathology Whole Slide Image (WSI) analysis, such a paradigm still faces performance and …
Pathology Whole Slide Image (WSI) analysis, such a paradigm still faces performance and …
Rethinking the learning paradigm for dynamic facial expression recognition
Abstract Dynamic Facial Expression Recognition (DFER) is a rapidly developing field that
focuses on recognizing facial expressions in video format. Previous research has …
focuses on recognizing facial expressions in video format. Previous research has …
Multiple instance learning framework with masked hard instance mining for whole slide image classification
The whole slide image (WSI) classification is often formulated as a multiple instance
learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WSI …
learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WSI …
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 for digital pathology: A review of the state-of-the-art, limitations & future potential
M Gadermayr, M Tschuchnig - Computerized Medical Imaging and …, 2024 - Elsevier
Digital whole slides images contain an enormous amount of information providing a strong
motivation for the development of automated image analysis tools. Particularly deep neural …
motivation for the development of automated image analysis tools. Particularly deep neural …
Morphological prototyping for unsupervised slide representation learning in computational pathology
Abstract Representation learning of pathology whole-slide images (WSIs) has been has
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …
primarily relied on weak supervision with Multiple Instance Learning (MIL). However the …