Multi-task deep learning for medical image computing and analysis: A review
The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …
conventional deep learning models are constructed for a single specific task, multi-task deep …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
A multimodal generative AI copilot for human pathology
Computational pathology, has witnessed considerable progress in the development of both
task-specific predictive models and task-agnostic self-supervised vision encoders …
task-specific predictive models and task-agnostic self-supervised vision encoders …
Towards a general-purpose foundation model for computational pathology
Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks,
requiring the objective characterization of histopathological entities from whole-slide images …
requiring the objective characterization of histopathological entities from whole-slide images …
[HTML][HTML] Cellvit: Vision transformers for precise cell segmentation and classification
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images
are important clinical tasks and crucial for a wide range of applications. However, it is a …
are important clinical tasks and crucial for a wide range of applications. However, it is a …
A general-purpose self-supervised model for computational pathology
Tissue phenotyping is a fundamental computational pathology (CPath) task in learning
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
objective characterizations of histopathologic biomarkers in anatomic pathology. However …
Weakly supervised joint whole-slide segmentation and classification in prostate cancer
The identification and segmentation of histological regions of interest can provide significant
support to pathologists in their diagnostic tasks. However, segmentation methods are …
support to pathologists in their diagnostic tasks. However, segmentation methods are …
Unleashing the potential of AI for pathology: challenges and recommendations
Computational pathology is currently witnessing a surge in the development of AI
techniques, offering promise for achieving breakthroughs and significantly impacting the …
techniques, offering promise for achieving breakthroughs and significantly impacting the …
A foundational multimodal vision language AI assistant for human pathology
The field of computational pathology has witnessed remarkable progress in the
development of both task-specific predictive models and task-agnostic self-supervised vision …
development of both task-specific predictive models and task-agnostic self-supervised vision …
Machine learning in computational histopathology: Challenges and opportunities
Digital histopathological images, high‐resolution images of stained tissue samples, are a
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …
vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state …