arXiv subjects

Masahiro Saiko

Publications and source records attributed to Masahiro Saiko.

3 recordsLinked to original sources

arXiv2026

Findings-aware Language-Image Pretraining for CT Volume Interpretation

Computed tomography (CT) provides diverse patient information and various clinical tasks, for which numerous deep learning models have been investigated. Recent years have seen the development of vision-language foundation models that can comprehensively extract clinical features, driven by representation learning that combines CT volumes with radiology reports. Existing approaches typically adopt Contrastive Language-Image Pre-training (CLIP)-based representation learning, where pairs from different cases are, in principle, treated as negative pairs such that the volume and report embeddings are pushed apart. In the biomedical imaging, however, treating different cases as negative pairs is not always appropriate, particularly when clinical similarity exists. In this study, we propose a supervised representation learning framework grounded in clinical knowledge from language foundation models, rather than the weak supervision used in CLIP. Specifically, we utilized a large language model to extract abnormal finding phrases from reports. We then explicitly constructed the target features that the image encoder should output, as the semantic representation of the extracted findings using a biomedical text embedding model. Our Findings-aware Language-Image Pre-training (FiLIP) outperformed weakly supervised approaches in multi-label zero-shot finding classification in both in-domain and external-domain evaluations, and also proved effective when fine-tuned for the downstream task of radiology report generation.

cs.CV↗

arXiv2026

Peripheral prototypical contrastive loss for supervised medical image segmentation

Robust segmentation of biomedical images remains challenging primarily due to ill-defined object boundaries. Although several loss functions have recently been proposed to enhance object boundary recognition, concerns remain about the susceptibility to unstable annotations resulting from boundary ambiguities. Here, we introduce the Peripheral Prototypical Contrastive (PPC) loss, which leverages prototypical contrastive learning to improve boundary recognition and enable stable training even under noisy annotation conditions. The PPC loss utilizes prototype features obtained from local regions near object boundaries, focusing specifically on ambiguous areas that have been challenging for existing approaches. Extensive experiments across multiple datasets demonstrated that the PPC loss consistently improve segmentation performance even under conditions with noisy labels. A newly proposed strategy, which adaptively determines the local region for prototype calculation, enhanced robustness and outperformed existing methods. Our findings indicate the potential of the PPC loss in facilitating scalable and reliable medical image segmentation, including the development of foundation models with imperfect annotations.

cs.CV↗

arXiv2026

Sam-Correction: Fully Adaptive Label Noise Reduction for Medical Image Segmentation

The quality of the teacher label has a dramatic impact on the outcome of the machine learning process. Medical image segmentation often suffers from a noisy ground truth (GT) mask due to difficulties in consistent annotation. Although a recent study has proposed a promising method to curate the noisy GT mask, this requires additional well-curated GT masks that are not always accessible without the aid of multiple medical experts. As an alternative, we propose SAM-Correction, which utilizes a foundation model to correct the noisy GT mask in a fully adaptive manner instead of manually preparing well-curated masks. In our approach, we progressively mitigate label noise via the segment anything model (SAM), where prompts are given from another segmentation network that we train concurrently. Experiments with various types of label noise derived from actual human annotations demonstrate that SAM-C achieves precise noise reduction and high-performing segmentation models trained thereby. Our method paves the way for robust model training even when clean labels are entirely unavailable.

cs.CV↗