Pathology

Latest AI and machine learning research in pathology for healthcare professionals.

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Cytoarchitecture in Words: Weakly Supervised Vision-Language Modeling for Human Brain Microscopy

Foundation models increasingly offer potential to support interactive, agentic workflows that assist...

PGVMS: A Prompt-Guided Unified Framework for Virtual Multiplex IHC Staining with Pathological Semantic Learning

Immunohistochemical (IHC) staining enables precise molecular profiling of protein expression, with o...

Enabling clinical use of foundation models in histopathology

Foundation models in histopathology are expected to facilitate the development of high-performing an...

Large-Language Models for data extraction from written kidney biopsy reports

Introduction: Kidney biopsy reports contain rich information that is clinically actionable and usefu...

Virtual Biopsy for Intracranial Tumors Diagnosis on MRI

Deep intracranial tumors situated in eloquent brain regions controlling vital functions present crit...

CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis

Foundation models have recently achieved impressive success in computational pathology, demonstratin...

When LoRA Betrays: Backdooring Text-to-Image Models by Masquerading as Benign Adapters

Low-Rank Adaptation (LoRA) has emerged as a leading technique for efficiently fine-tuning text-to-im...

Momentum Memory for Knowledge Distillation in Computational Pathology

Multimodal learning that integrates genomics and histopathology has shown strong potential in cancer...

Benchmarking Transfer Learning for Dense Breast Tissue Segmentation on Small Mammogram Datasets

Dense breast tissue diminishes the sensitivity of mammographic screening and is a key cancer risk fa...

Morphological set enrichment enables interpretable prognostication and molecular profiling of meningiomas

Meningiomas are the most common primary brain tumors and, despite their benign reputation, often beh...

Pixel2Gene enables histology-guided reconstruction and prediction of spatial gene expression

Advances in spatial transcriptomics (ST) have fundamentally transformed our understanding of tissue ...

GUIDE-US: Grade-Informed Unpaired Distillation of Encoder Knowledge from Histopathology to Micro-UltraSound

Purpose: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedi...

Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy

Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recogn...

US-JEPA: A Joint Embedding Predictive Architecture for Medical Ultrasound

Ultrasound (US) imaging poses unique challenges for representation learning due to its inherently no...

Reconstructing multi-scale tissue spatial architecture from single-cell RNA-seq with REMAP

Understanding spatial organization of cells is critical for deciphering tissue function and disease....

Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma

Background: Current risk stratification for Ewing sarcoma relies heavily on clinical factors such as...

RamanSeg: Interpretability-driven Deep Learning on Raman Spectra for Cancer Diagnosis

Histopathology, the current gold standard for cancer diagnosis, involves the manual examination of t...

CardioPulmoNet: Modeling Cardiopulmonary Dynamics for Histopathological Diagnosis

Objective: This study investigates whether incorporating physiological coupling concepts into neural...

BEEP Learning: Multi-View Image Decomposition for Massively Multiplexed Biological Fluorescence Microscopy

Fluorescence imaging with spectrally variant fluorophores allows the spatial mapping of biological s...

LGD-Net: Latent-Guided Dual-Stream Network for HER2 Scoring with Task-Specific Domain Knowledge

It is a critical task to evalaute HER2 expression level accurately for breast cancer evaluation and ...

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