A5019616787- AI in cancer detection
- Biomedical Text Mining and Ontologies
- Radiomics and Machine Learning in Medical Imaging
- Digital Imaging for Blood Diseases
- Multiple Myeloma Research and Treatments
- Genetics, Bioinformatics, and Biomedical Research
- Malaria Research and Control
- Computational Drug Discovery Methods
- Brain Tumor Detection and Classification
- Ethics in Clinical Research
- Mosquito-borne diseases and control
- Chemical Synthesis and Analysis
- Artificial Intelligence in Healthcare and Education
- Medical Imaging and Analysis
- Artificial Intelligence in Healthcare
- Cell Image Analysis Techniques
Brigham and Women's Hospital
2024
Harvard University
2023-2024
Dana-Farber Cancer Institute
2024
Harvard–MIT Division of Health Sciences and Technology
2024
Massachusetts Institute of Technology
2023-2024
Broad Institute
2024
Human tissue, which is inherently three-dimensional (3D), traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation hampered by complex manual evaluation and lack of computational platforms distill insights from large, high-resolution datasets. We present...
Foundation models are reshaping computational pathology by enabling transfer learning, where pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks. Despite these advances, foundation still limited in their ability to encode the entire gigapixel whole-slide images without additional training often lack complementary multimodal data. Here, we introduce Threads, a slide-level model capable of generating universal representations any...
The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient slide level remains constrained by limited data disease-specific cohorts, especially for rare conditions. We propose TITAN, a multimodal whole model...
Indisulam, a DCAF15-based molecular glue degrader, induces widespread proteome changes with implications for cell division and chromosome segregation. While RBM39 RBM23 are two well-characterized indisulam neo-substrates, additional targets likely exist. To identify those degradation targets, we applied network-based approach to prioritize novel neo-substrates from large-scale omics data. Our integrates proteome-wide expression measurements information publicly accessible databases into...
Abstract Glioblastoma displays morphological heterogeneity on routine H&E whole slide images, with conflicting evidence the reliability of features in predicting tumor behavior and treatment response. However, current standards for assessing morphology disease status rely nonquantitative manual evaluation by pathologists, which are prone to inherent variation. We sought (1) expand tools available quantitative assessment (2) evaluate utility this approach identifying valuable biomarkers....