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Found 7 papers, 4 papers with code
NASDM: Nuclei-Aware Semantic Histopathology Image Generation Using Diffusion Models
In recent years, computational pathology has seen tremendous progress driven by deep learning methods in segmentation and classification tasks aiding prognostic and diagnostic settings.
Improving Interpretability via Explicit Word Interaction Graph Layer
Recent NLP literature has seen growing interest in improving model interpretability.
CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision
We propose CLIP-Lite, an information efficient method for visual representation learning by feature alignment with textual annotations.
Estimating and Maximizing Mutual Information for Knowledge Distillation
Our empirical results show that MIMKD outperforms competing approaches across a wide range of student-teacher pairs with different capacities, with different architectures, and when student networks are with extremely low capacity.
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification
We regularized the clustering mechanism by introducing a KL-divergence loss between the attention weights of patches in a cluster and the uniform distribution.
Self-Attentive Adversarial Stain Normalization
We propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain.
Deep Learning for Visual Recognition of Environmental Enteropathy and Celiac Disease
Physicians use biopsies to distinguish between different but histologically similar enteropathies.
