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Found 7 papers, 3 papers with code
Task-oriented Embedding Counts: Heuristic Clustering-driven Feature Fine-tuning for Whole Slide Image Classification
To address this issue, we propose a heuristic clustering-driven feature fine-tuning method (HC-FT) to enhance the performance of multiple instance learning by providing purified positive and hard negative samples.
ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception
Protein function prediction is currently achieved by encoding its sequence or structure, where the sequence-to-function transcendence and high-quality structural data scarcity lead to obvious performance bottlenecks.
RetMIL: Retentive Multiple Instance Learning for Histopathological Whole Slide Image Classification
At the local level, the WSI sequence is divided into multiple subsequences.
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis
Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing.
Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classification
While most of the conventional MIL methods use attention scores to estimate instance importance scores (IIS) which contribute to the prediction of the slide labels, these often lead to skewed attention distributions and inaccuracies in identifying crucial instances.
Introducing π-HelixNovo for practical large-scale de novo peptide sequencing
De novo peptide sequencing is a promising approach for novel peptide discovery.
The Whole Pathological Slide Classification via Weakly Supervised Learning
Due to its superior efficiency in utilizing annotations and addressing gigapixel-sized images, multiple instance learning (MIL) has shown great promise as a framework for whole slide image (WSI) classification in digital pathology diagnosis.
