[HTML][HTML] Beyond Supervised: The Rise of Self-Supervised Learning in Autonomous Systems
H Taherdoost - Information, 2024 - mdpi.com
Supervised learning has been the cornerstone of many successful medical imaging
applications. However, its reliance on large labeled datasets poses significant challenges …
applications. However, its reliance on large labeled datasets poses significant challenges …
Exploring Multiple Instance Learning (MIL): A brief survey
Abstract Multiple Instance Learning (MIL) is a learning paradigm, where training instances
are arranged in sets, called bags, and only bag-level labels are available during training …
are arranged in sets, called bags, and only bag-level labels are available during training …
E2-MIL: An explainable and evidential multiple instance learning framework for whole slide image classification
Multiple instance learning (MIL)-based methods have been widely adopted to process the
whole slide image (WSI) in the field of computational pathology. Due to the sparse slide …
whole slide image (WSI) in the field of computational pathology. Due to the sparse slide …
[HTML][HTML] Retrosynthetic analysis via deep learning to improve pilomatricoma diagnoses
Z Wang, X Tan, X Yang, H Hu, K Lin, C Wang… - Computers in Biology …, 2024 - Elsevier
Background Pilomatricoma, a benign childhood skin tumor, presents diagnostic challenges
due to its manifestation variations and requires surgical excision upon histological …
due to its manifestation variations and requires surgical excision upon histological …
Advancements in computer vision and pathology: Unraveling the potential of artificial intelligence for precision diagnosis and beyond.
J Chang, B Hatfield - Advances in Cancer Research, 2024 - europepmc.org
The integration of computer vision into pathology through slide digitalization represents a
transformative leap in the field's evolution. Traditional pathology methods, while reliable, are …
transformative leap in the field's evolution. Traditional pathology methods, while reliable, are …
Tissue Concepts: supervised foundation models in computational pathology
T Nicke, JR Schaefer, H Hoefener, F Feuerhake… - arXiv preprint arXiv …, 2024 - arxiv.org
Due to the increasing workload of pathologists, the need for automation to support
diagnostic tasks and quantitative biomarker evaluation is becoming more and more …
diagnostic tasks and quantitative biomarker evaluation is becoming more and more …
HistoPlexer: Histopathology-based Protein Multiplex Generation using Deep Learning
Multiplexed imaging technologies provide crucial insights into interactions between tumors
and their surrounding tumor microenvironment (TME), but their widespread adoption is …
and their surrounding tumor microenvironment (TME), but their widespread adoption is …
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
We present a pioneering investigation into the application of deep learning techniques to
analyze histopathological images for addressing the substantial challenge of automated …
analyze histopathological images for addressing the substantial challenge of automated …
Histopathology Foundation Models Enable Accurate Ovarian Cancer Subtype Classification
Large pretrained transformers are increasingly being developed as generalised foundation
models which can underpin powerful task-specific artificial intelligence models …
models which can underpin powerful task-specific artificial intelligence models …
Genomics-Guided Representation Learning for Pathologic Pan-Cancer Tumor Microenvironment Subtype Prediction
Abstract The characterization of Tumor MicroEnvironment (TME) is challenging due to its
complexity and heterogeneity. Relatively consistent TME characteristics embedded within …
complexity and heterogeneity. Relatively consistent TME characteristics embedded within …