8 citations · 8 across the 3 of their papers we have counts for
5 papers
Hyperparameter-Free Boundary-Interior t-vMF Dice Loss for Medical Image Segmentation
Hinako Mitsuoka, Kazuhiro Hotta
Class imbalance and errors near anatomical boundaries remain challenges in medical image segmentation, motivating loss functions that account for these difficulties. Adaptive t-vMF…
Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters
Hinako Mitsuoka, Kazuhiro Hotta
Segment Anything Model 2 (SAM2) demonstrated impressive zero-shot capabilities on natural images but faces challenges in biomedical segmentation due to significant domain shifts an…
Combining Boundary Supervision and Segment-Level Regularization for Fine-Grained Action Segmentation
Hinako Mitsuoka, Kazuhiro Hotta
Recent progress in Temporal Action Segmentation (TAS) has increasingly relied on complex architectures, which can hinder practical deployment. We present a lightweight dual-loss tr…
Accuracy Improvement of Cell Image Segmentation Using Feedback Former
Hinako Mitsuoka, Kazuhiro Hotta
Semantic segmentation of microscopy cell images by deep learning is a significant technique. We considered that the Transformers, which have recently outperformed CNNs in image rec…
Generalized SAM: Efficient Fine-Tuning of SAM for Variable Input Image Sizes
Sota Kato, Hinako Mitsuoka, Kazuhiro Hotta
There has been a lot of recent research on improving the efficiency of fine-tuning foundation models. In this paper, we propose a novel efficient fine-tuning method that allows the…