1 citations · 1 across the 6 of their papers we have counts for
6 papers
EXAONE Path 2.5: Pathology Foundation Model with Multi-Omics Alignment
Juseung Yun, Sunwoo Yu, Sumin Ha +4
Cancer progression arises from interactions across multiple biological layers, especially beyond morphological and across molecular layers that remain invisible to image-only model…
EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision
Myeongjang Pyeon, Janghyeon Lee, Minsoo Lee +7
In digital pathology, whole-slide images (WSIs) are often difficult to handle due to their gigapixel scale, so most approaches train patch encoders via self-supervised learning (SS…
ChatEXAONEPath: An Expert-level Multimodal Large Language Model for Histopathology Using Whole Slide Images
Sangwook Kim, Soonyoung Lee, Jongseong Jang
Recent studies have made significant progress in developing large language models (LLMs) in the medical domain, which can answer expert-level questions and demonstrate the potentia…
EXAONEPath 1.0 Patch-level Foundation Model for Pathology
Juseung Yun, Yi Hu, Jinhyung Kim +2
Recent advancements in digital pathology have led to the development of numerous foundational models that utilize self-supervised learning on patches extracted from gigapixel whole…
Pan-cancer gene set discovery via scRNA-seq for optimal deep learning based downstream tasks
Jong Hyun Kim, Jongseong Jang
The application of machine learning to transcriptomics data has led to significant advances in cancer research. However, the high dimensionality and complexity of RNA sequencing (R…
Exploring the Spectrum of Visio-Linguistic Compositionality and Recognition
Youngtaek Oh, Pyunghwan Ahn, Jinhyung Kim +4
Vision and language models (VLMs) such as CLIP have showcased remarkable zero-shot recognition abilities yet face challenges in visio-linguistic compositionality, particularly in l…