2 citations · 3 across the 9 of their papers we have counts for
6 papers · 1 filter
LRMIL: Efficient Low-Resolution Multiple Instance Learning via High-Resolution Knowledge Distillation for Whole Slide Image Classification
Yonghan Shin, Won-Ki Jeong
Multiple instance learning (MIL) has become a standard paradigm for whole slide image (WSI) analysis in digital pathology, as it enables slide-level prediction without dense annota…
MC-INR: Efficient Encoding of Multivariate Scientific Simulation Data using Meta-Learning and Clustered Implicit Neural Representations
Hyunsoo Son, Jeonghyun Noh, Suemin Jeon +2
Implicit Neural Representations (INRs) are widely used to encode data as continuous functions, enabling the visualization of large-scale multivariate scientific simulation data wit…
Synthetic Data Augmentation using Pre-trained Diffusion Models for Long-tailed Food Image Classification
GaYeon Koh, Hyun-Jic Oh, Jeonghyun Noh +1
Deep learning-based food image classification enables precise identification of food categories, further facilitating accurate nutritional analysis. However, real-world food images…
Controllable and Efficient Multi-Class Pathology Nuclei Data Augmentation using Text-Conditioned Diffusion Models
Hyun-Jic Oh, Won-Ki Jeong
In the field of computational pathology, deep learning algorithms have made significant progress in tasks such as nuclei segmentation and classification. However, the potential of…
Co-synthesis of Histopathology Nuclei Image-Label Pairs using a Context-Conditioned Joint Diffusion Model
Seonghui Min, Hyun-Jic Oh, Won-Ki Jeong
In multi-class histopathology nuclei analysis tasks, the lack of training data becomes a main bottleneck for the performance of learning-based methods. To tackle this challenge, pr…
Evaluation and improvement of Segment Anything Model for interactive histopathology image segmentation
SeungKyu Kim, Hyun-Jic Oh, Seonghui Min +1
With the emergence of the Segment Anything Model (SAM) as a foundational model for image segmentation, its application has been extensively studied across various domains, includin…