Results 1 to 10 of about 698,939 (333)
Quilt-1M: One Million Image-Text Pairs for Histopathology [PDF]
Neural Information Processing Systems, 2023 Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online. However, the scarcity of analogous data in the medical field, specifically in histopathology, has halted comparable ...
W. Ikezogwo +7 more
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DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification [PDF]
Computer Vision and Pattern Recognition, 2022 Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs). However, MIL approaches for this specific classification problem still face unique challenges, particularly those related to ...
Hongrun Zhang +6 more
semanticscholar +1 more source
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images [PDF]
Computer Vision and Pattern Recognition, 2023 Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities.
Ming Y. Lu +8 more
semanticscholar +1 more source
Quilt-LLaVA: Visual Instruction Tuning by Extracting Localized Narratives from Open-Source Histopathology Videos [PDF]
Computer Vision and Pattern Recognition, 2023 Diagnosis in histopathology requires a global whole slide images (WSIs) analysis, requiring pathologists to compound evidence from different WSI patches. The gigapixel scale of WSIs poses a challenge for histopathology multimodal models.
M. S. Seyfioglu +4 more
semanticscholar +1 more source
PathLDM: Text conditioned Latent Diffusion Model for Histopathology [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2023 To achieve high-quality results, diffusion models must be trained on large datasets. This can be notably prohibitive for models in specialized domains, such as computational pathology. Conditioning on labeled data is known to help in data-efficient model
Srikar Yellapragada +5 more
semanticscholar +1 more source
Proceedings, 2021 Introduction: Pediatric renal tumors constitute 7 to 8% of pediatric solid malignancies and most common is Wilms tumor. It usually presents as unilateral mass with sporadic and familial associations.
Fariha Sahrish +5 more
doaj +1 more source
EClinicalMedicine, 2022 Summary: Background: Pregnant women with SARS-CoV-2 infection experience higher rates of stillbirth and preterm birth. A unique pattern of chronic histiocytic intervillositis (CHI) and/or massive perivillous fibrin deposition (MPFD) has emerged, coined ...
Sophie Stenton +19 more
doaj +1 more source
INI-1-Deficient Sinonasal Carcinoma: Case Report with Emphasis on Differential Diagnosis
Case Reports in Pathology, 2022 SMARCB1-deficient sinonasal carcinoma is a newly described entity, with less than 100 reported cases. It is characterized by basaloid or rhabdoid morphology and is diagnosed by complete loss of nuclear SMARCB1 (INI-1).
Anwaar M. Alsayed +4 more
doaj +1 more source
A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images [PDF]
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2022 Visual microscopic study of diseased tissue by pathologists has been the cornerstone for cancer diagnosis and prognostication for more than a century. Recently, deep learning methods have made significant advances in the analysis and classification of ...
Puria Azadi Moghadam +6 more
semanticscholar +1 more source
Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling
medRxiv, 2023 Computational pathology is revolutionizing the field of pathology by integrating advanced computer vision and machine learning technologies into diagnostic workflows.
Alexandre Filiot +7 more
semanticscholar +1 more source