Search Results for author: Sophia J. Wagner

Found 12 papers, 9 papers with code

Molecular-driven Foundation Model for Oncologic Pathology

no code implementations28 Jan 2025 Anurag Vaidya, Andrew Zhang, Guillaume Jaume, Andrew H. Song, Tong Ding, Sophia J. Wagner, Ming Y. Lu, Paul Doucet, Harry Robertson, Cristina Almagro-Perez, Richard J. Chen, Dina ElHarouni, Georges Ayoub, Connor Bossi, Keith L. Ligon, Georg Gerber, Long Phi Le, Faisal Mahmood

Foundation models are reshaping computational pathology by enabling transfer learning, where models pre-trained on vast datasets can be adapted for downstream diagnostic, prognostic, and therapeutic response tasks.

Benchmarking model +3

Multimodal Whole Slide Foundation Model for Pathology

1 code implementation29 Nov 2024 Tong Ding, Sophia J. Wagner, Andrew H. Song, Richard J. Chen, Ming Y. Lu, Andrew Zhang, Anurag J. Vaidya, Guillaume Jaume, Muhammad Shaban, Ahrong Kim, Drew F. K. Williamson, Bowen Chen, Cristina Almagro-Perez, Paul Doucet, Sharifa Sahai, Chengkuan Chen, Daisuke Komura, Akihiro Kawabe, Shumpei Ishikawa, Georg Gerber, Tingying Peng, Long Phi Le, Faisal Mahmood

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).

Cross-Modal Retrieval model +4

DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology

1 code implementation7 Apr 2024 Valentin Koch, Sophia J. Wagner, Salome Kazeminia, Ece Sancar, Matthias Hehr, Julia Schnabel, Tingying Peng, Carsten Marr

In hematology, computational models offer significant potential to improve diagnostic accuracy, streamline workflows, and reduce the tedious work of analyzing single cells in peripheral blood or bone marrow smears.

Multiple Instance Learning Transfer Learning

Low-resource finetuning of foundation models beats state-of-the-art in histopathology

1 code implementation9 Jan 2024 Benedikt Roth, Valentin Koch, Sophia J. Wagner, Julia A. Schnabel, Carsten Marr, Tingying Peng

Recently, foundation models in computer vision showed that leveraging huge amounts of data through supervised or self-supervised learning improves feature quality and generalizability for a variety of tasks.

Self-Supervised Learning Weakly-supervised Learning +1

Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction

1 code implementation13 May 2022 Daniel Reisenbüchler, Sophia J. Wagner, Melanie Boxberg, Tingying Peng

Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond individual entities.

Inductive Bias Multiple Instance Learning +1

S5CL: Unifying Fully-Supervised, Self-Supervised, and Semi-Supervised Learning Through Hierarchical Contrastive Learning

1 code implementation14 Mar 2022 Manuel Tran, Sophia J. Wagner, Melanie Boxberg, Tingying Peng

Evaluations of our framework on two public histopathological datasets show strong improvements in the case of sparse labels: for a H&E-stained colorectal cancer dataset, the accuracy increases by up to 9% compared to supervised cross-entropy loss; for a highly imbalanced dataset of single white blood cells from leukemia patient blood smears, the F1-score increases by up to 6%.

Contrastive Learning

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