2 citations · 3 across the 7 of their papers we have counts for
7 papers
FFM-CP: Cross-Backbone Fusion of Vision-Language Foundation Models for Few-Shot Computational Pathology
Anh-Tien Nguyen, Trung DQ. Dang, Nghiem Tuong Diep +9
Pathology vision-language foundation models vary in performance across diseases and tasks, with no single model consistently performing best. The high cost of expert pathology anno…
Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
Tan-Ha Mai, Chao-Kai Chiang, Han-Hwa Shih +3
Complementary-label learning (CLL) is a weakly supervised paradigm where instances are labeled with classes they do not belong to. Despite a decade of research, CLL methods remain…
Revolutionizing Precise Low Back Pain Diagnosis via Contrastive Learning
Thanh Binh Le, Hoang Nhat Khang Vo, Tan-Ha Mai +1
Low back pain affects millions worldwide, driving the need for robust diagnostic models that can jointly analyze complex medical images and accompanying text reports. We present Lu…
Intra-Cluster Mixup: An Effective Data Augmentation Technique for Complementary-Label Learning
Tan-Ha Mai, Hsuan-Tien Lin
In this paper, we investigate the challenges of complementary-label learning (CLL), a specialized form of weakly-supervised learning (WSL) where models are trained with labels indi…
libcll: an Extendable Python Toolkit for Complementary-Label Learning
Nai-Xuan Ye, Tan-Ha Mai, Hsiu-Hsuan Wang +2
Complementary-label learning (CLL) is a weakly supervised learning paradigm for multiclass classification, where only complementary labels -- indicating classes an instance does no…
From SMOTE to Mixup for Deep Imbalanced Classification
Wei-Chao Cheng, Tan-Ha Mai, Hsuan-Tien Lin
Given imbalanced data, it is hard to train a good classifier using deep learning because of the poor generalization of minority classes. Traditionally, the well-known synthetic min…
CLImage: Human-Annotated Datasets for Complementary-Label Learning
Hsiu-Hsuan Wang, Tan-Ha Mai, Nai-Xuan Ye +2
Complementary-label learning (CLL) is a weakly-supervised learning paradigm that aims to train a multi-class classifier using only complementary labels, which indicate classes to w…