News | MacsLAB

Congratulations on CVPR 2026 Acceptance!

Sat, 21 Feb 2026 00:00:00 +0000

We are excited to announce that our paper has been accepted to CVPR 2026:

Yeongsu Kim, Seo-Yeon Choi, and Kyungsu Lee, “Human-Intervention Segmentation via Federated Intent Embedding and Multi-Mask Recommendation.”

Venue: CVPR 2026 (Conference)
Subject Area: Vision applications and systems
Keywords: Computer Vision, Machine Learning, User Experience Design
Student Paper: Yes

Abstract:

Artificial intelligence (AI) has advanced radiology, yet variability across hospitals and devices undermines reliability and trust. We present a federated learning framework that combines frequency-domain harmonization and instruction-conditioned personalization to deliver consistent and interpretable diagnostic outcomes. Using FFT-based reconstructions informed by radiomics descriptors, the system reduces equipment dependency, while CLIP-based text conditioning enables clinicians to guide reconstructions to local practices and patient needs. We evaluated the framework across four hospitals with fifteen radiologists and fifty patients, spanning polyp detection, rotator cuff tear diagnosis, pneumothorax classification, and breast cancer classification/segmentation. Results show significant gains in accuracy, calibration, and robustness under cross-site transfer, without introducing prohibitive latency. Radiologists reported improved interpretability and preserved professional agency, while patients expressed greater trust, reduced anxiety, and stronger acceptance of AI involvement. This work advances a human-centered design for medical AI, aligning federated learning with transparency, equity, and trustworthy deployment.

Congratulations to the authors on this excellent result.

Congratulations on AISTATS 2026 Acceptance!

Sun, 01 Feb 2026 00:00:00 +0000

We are excited to share that our paper has been accepted to AISTATS 2026:

Seo-Yeon Choi, and Kyungsu Lee*, “TCP: Context-Aware Pooling via Top-k% Activation Selection,” Annual Conference on Artificial Intelligence and Statistics (AISTATS 2026).

This is a strong result at a Top BK/CS venue, and we warmly congratulate Seo-Yeon Choi (first author) and Kyungsu Lee (corresponding author).

Venue: AISTATS 2026
Badge: Top
Badge: BK/CS

Congratulations again to the authors on this excellent achievement.

Congratulations on CHI 2026 Acceptance

Thu, 22 Jan 2026 00:00:00 +0000

We are delighted to announce that our paper has been accepted to CHI 2026:

Seo-Yeon Choi, and Kyungsu Lee*, “Human-Centered Personalization in Radiology AI: Evaluating Trust, Usability, and Cross-Hospital Robustness,” ACM CHI Conference on Human Factors in Computing Systems (CHI 2026).

This paper was accepted to a Top BK/CS conference, and we sincerely congratulate Seo-Yeon Choi (first author) and Kyungsu Lee (corresponding author).

Venue: CHI 2026
Badge: Top
Badge: BK/CS

Congratulations to the authors for this outstanding milestone.

(2025 KOSOMBE) Sagang Hong Won Best Poster Award

Sat, 08 Nov 2025 00:00:00 +0000

Congratulations!

Sagang Hong, a Master’s student at MacsLAB, won the Best Poster Award at the 2025 Fall Conference of the Korean Society of Medical and Biological Engineering (KOSOMBE).

This achievement recognizes his outstanding research presented at the conference held from November 6 to 8, 2025, at Inje University (Gimhae).

KOSOMBE 2025 Fall Conference Award Ceremony

Best Poster Award Certificate for Sagang Hong

Award-winning Paper Information:

Sagang Hong (1st author), Junyoung Kim, Kyungsu Lee (Corresponding author)
SAM2-based Bayesian Prompt Adaptation for Cross-Modality Medical Segmentation

We sincerely congratulate Sagang Hong on his award and look forward to more excellent research achievements from MacsLAB.

(AIxMHC 2025) Best Poster Award

Wed, 15 Oct 2025 00:00:00 +0000

Congratulations!

The team of Seo-Yeon Choi, Haeyun Lee, and Kyungsu Lee from MacsLAB won the Best Poster Award at AIxMHC 2025.

The award-winning paper is:

Statistical Multi-Modal Fusion for Patient-Centric Medical Diagnosis Using DICOM

AIxMHC 2025 Best Poster Award Certificate

AIxMHC 2025 Team Photo

Poster Presentation Scene

MacsLAB will continue to strive for clinically meaningful research achievements in the field of medical AI.

Congratulations to Seo-Yeon Choi (Student Researcher, Undergraduate Researcher) on Two Papers Accepted to ICCV 2025 Workshops!

Sat, 19 Jul 2025 00:00:00 +0000

We are thrilled to announce that our undergraduate researcher, Seo-Yeon Choi, has achieved a remarkable accomplishment—two papers have been accepted to workshops at ICCV 2025 Workshops (CVAMD / VADH25)!

Even more exciting, one paper was selected for an oral presentation and the other for a poster presentation. Having two papers accepted at such a prestigious venue as ICCV is a truly outstanding feat, especially for an undergraduate researcher. This is a testament to Seo-Yeon’s dedication, hard work, and innovative research.

Congratulations once again, Seo-Yeon! We look forward to seeing both the oral and poster presentations at ICCV 2025 in Hawaii! 🌺🌴

Abstract

Deep learning (DL) has led to substantial progress in medical image analysis, particularly for disease classification. However, the integration of patient-specific attributes, such as age, body mass index (BMI), and lifestyle factors with radiomics and raw imaging data remains a key challenge in the development of personalized diagnostic models. To alleviate this, in this research, we propose a novel multi-modal framework, denoted as Statistically Coherent Network (SCN), which jointly models imaging, radiomic, and patient metadata through a structured multi-space latent representation. SCN facilitates distributional coherence across subpopulations by leveraging a newly devised statistics-based loss in conjunction with a triplet loss, thereby aligning feature distributions among clinically similar cohorts. This statistical alignment using T-test facilitates more interpretable and robust representation learning across heterogeneous patient groups. We evaluate SCN on four clinically diverse tasks, including breast cancer (mammography), obstructive sleep apnea (CT), rotator cuff tear (MRI), and Cormack-Lehane grading (X-ray), and demonstrate the consistent improvements over conventional single-space and multi-modal baselines. The experimental results highlight the importance of explicitly incorporating patient metadata, in terms of multimodal learning, to enhance model generalizability and clinical relevance.

Memory-Guided Personalization for Physician-Specific Diagnostic Inference

Abstract

Recent advances in deep learning have improved diagnostic precision across medical imaging tasks. However, clinical adoption remains limited due to a mismatch between model outputs and the diverse reasoning styles of physicians. Prior personalization efforts have primarily focused on patient-specific adaptation, overlooking clinician-specific variability. We propose a physician-centric diagnostic framework that supports real-time, adaptive inference tailored to individual clinicians. The system consists of three stages: supervised learning, Human-in-the-Loop guidance, and personalized deployment. Physician feedback is encoded as memory-based priors and reused at inference without retraining, enabling lightweight, end-to-end personalization. We validate our method on detection and segmentation tasks including parathyroid localization, breast cancer segmentation, and rotator cuff tear analysis. Results demonstrated that our model adapts effectively to individual diagnostic styles while maintaining high accuracy in clinical workflows.

Once again, congratulations to Seo-Yeon Choi for this outstanding achievement. Let’s look forward to an inspiring and impactful presentation at ICCV 2025! 🚀🎉

Congratulations to Yeongsu Kim (Student Researcher, Undergraduate Researcher) on ICLR 2025 Workshop Acceptance!

Mon, 03 Mar 2025 00:00:00 +0000

Exciting news! Our undergraduate researcher, Yeongsu Kim, has achieved an outstanding milestone—his paper has been accepted to the ML4RS Workshop at ICLR 2025!

As an undergraduate student, getting a paper into a prestigious venue like ICLR is no small feat, and this accomplishment is a testament to his dedication and hard work. Congratulations once again, Yeongsu!

Looking forward to seeing the research presented in Singapore this April! 🚀🎉

Abstract

Over the past few decades, geospatial objects have been extensively recognized as significant components in remote sensing applications, including environmental monitoring, urban planning, and defense. Particularly, accurate segmentation of objects has aimed at meaningful observations from aerial imagery, leading to the necessity of deep learning-based methodologies. However, conventional deep learning-based segmentation methodologies exhibit limited generalization capabilities across diverse geographical domains due to inherent variations in regional characteristics and data distribution shifts. Furthermore, most existing approaches strongly rely on static, pre-trained models lacking the adaptability to handle previously unseen data. To alleviate these limitations, we propose a novel Few-shot Semi-Online Adaptation framework incorporating interactive user feedback to iteratively refine segmentation outputs. By leveraging online learning and test-time adaptation, our approach enables models to continuously be accurate based on minimal user corrections, ensuring flexibility and adaptability to new environments. Experimental results demonstrate that our method effectively enhances the segmentation accuracy with minimal user intervention, bridging the gap between automated segmentation and domain-specific expertise. Our research contributes to the development of interactive, user-adaptive segmentation models to facilitate geospatial analysis more efficiently and reliably.

One Paper Accepted to ICLR 2025!

Thu, 23 Jan 2025 00:00:00 +0000

Thrilled to announce that our paper, “Connectome Mapping: Shape-Memory Network via Interpretation of Contextual Semantic Information,” has been accepted to ICLR 2025! See you in Singapore in April!

Abstract

Contextual semantic information plays a pivotal role in the brain’s visual interpretation of the surrounding environment. When processing visual information, electrical signals within synapses facilitate the dynamic activation and deactivation of synaptic connections, guided by the contextual semantic information associated with different objects. In the realm of Artificial Intelligence (AI), neural networks have emerged as powerful tools to emulate complex signaling systems, enabling tasks such as classification and segmentation by understanding visual information. However, conventional neural networks have limitations in simulating the conditional activation and deactivation of synapses, collectively known as the connectome, a comprehensive map of neural connections in the brain. Additionally, the pixel-wise inference mechanism of conventional neural networks failed to account for the explicit utilization of contextual semantic information in the prediction process. To overcome these limitations, we developed a novel neural network, dubbed the Shape Memory Network (SMN), which excels in two key areas: (1) faithfully emulating the intricate mechanism of the brain’s connectome, and (2) explicitly incorporating contextual semantic information during the inference process. The SMN memorizes the structure suitable for contextual semantic information and leverages this structure at the inference phase. The structural transformation emulates the conditional activation and deactivation of synaptic connections within the connectome. Rigorous experimentation carried out across a range of semantic segmentation benchmarks demonstrated the outstanding performance of the SMN, highlighting its superiority and effectiveness. Furthermore, our pioneering network on connectome emulation reveals the immense potential of the SMN for next-generation neural networks.

(Fall 2024) Awarded at K-Health Medical AI Hackathon

Wed, 20 Nov 2024 00:00:00 +0000

Congratulations!

Undergraduate researchers Dayoung Kang, Sumin Kim, Sehyun Park, and Seo-Yeon Choi from MacsLAB won the Grand Prize at the 2024 K-Health Medical AI Hackathon.

The competition focused on developing an AI model for breast mass segmentation using mammography image data. The students developed, trained, and tuned their own segmentation model, achieving high validation performance by utilizing public datasets in addition to the provided dataset, leading to their victory.

(Spring 2024) Joined Department of Computer Science & Artificial Intelligence, Jeonbuk National University

Fri, 08 Mar 2024 00:00:00 +0000

Professor Kyungsu Lee appointed to the Department of Computer Science & Artificial Intelligence, Jeonbuk National University.

News | MacsLAB

Congratulations on CVPR 2026 Acceptance!

Congratulations on AISTATS 2026 Acceptance!

Congratulations on CHI 2026 Acceptance

(2025 KOSOMBE) Sagang Hong Won Best Poster Award

(AIxMHC 2025) Best Poster Award

Congratulations to Seo-Yeon Choi (Student Researcher, Undergraduate Researcher) on Two Papers Accepted to ICCV 2025 Workshops!

Patient-Centric Statistical Multi-Modal Fusion for Medical Diagnosis: Integrating DICOM, Radiomics, and Patient Attributes

Abstract

Memory-Guided Personalization for Physician-Specific Diagnostic Inference

Abstract

Congratulations to Yeongsu Kim (Student Researcher, Undergraduate Researcher) on ICLR 2025 Workshop Acceptance!

Abstract

One Paper Accepted to ICLR 2025!

Abstract

(Fall 2024) Awarded at K-Health Medical AI Hackathon

(Spring 2024) Joined Department of Computer Science & Artificial Intelligence, Jeonbuk National University