6 papers · 1 filter
Leveraging Vision-Language Models as Weak Annotators in Active Learning
Phuong Ngoc Nguyen, Kaito Shiku, Ryoma Bise +2
Active learning aims to reduce annotation cost by selectively querying informative samples for supervision under a limited labeling budget. In this work, we investigate how vision-…
Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification
Shota Harada, Ryoma Bise, Kiyohito Tanaka +1
Semi-supervised domain adaptation leverages a few labeled and many unlabeled target samples, making it promising for addressing domain shifts in medical image analysis. However, ex…
Enhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection Module
Xiaotong Ji, Ryoma Bise, Seiichi Uchida
In medical image processing, accurate diagnosis is of paramount importance. Leveraging machine learning techniques, particularly top-rank learning, shows significant promise by foc…
Instance-wise Supervision-level Optimization in Active Learning
Shinnosuke Matsuo, Riku Togashi, Ryoma Bise +2
Active learning (AL) is a label-efficient machine learning paradigm that focuses on selectively annotating high-value instances to maximize learning efficiency. Its effectiveness c…
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity
Takeaki Kadota, Hideaki Hayashi, Ryoma Bise +2
Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve…
Pseudo-label Learning with Calibrated Confidence Using an Energy-based Model
Masahito Toba, Seiichi Uchida, Hideaki Hayashi
In pseudo-labeling (PL), which is a type of semi-supervised learning, pseudo-labels are assigned based on the confidence scores provided by the classifier; therefore, accurate conf…