사전 훈련된 모델을 사용한 이중 언어 텍스트 종속 화자 검증: TdSV Challenge 2024 참가 논문
사전 훈련된 모델을 활용한 독립적인 구문 및 화자 검증 시스템으로 TdSV Challenge 2024에서 경쟁력 있는 성능을 달성할 수 있습니다.
Bilingual Text-dependent Speaker Verification Using Pre-trained Models: A Winning Approach at the TdSV Challenge 2024
This paper describes a winning approach to the TdSV Challenge 2024, demonstrating that competitive performance in text-dependent speaker verification can be achieved using independent pre-trained models for phrase and speaker verification, without relying on joint modeling of speaker and text.
基於高斯混合變分自編碼器的 MRI 參數映射：打破獨立像素的假設
本文提出了一種基於深度學習的 MRI 參數映射新方法，它利用數據冗餘來改進定量參數映射，並證明了該方法在模擬和真實數據中的有效性。
SpineSegDiff: A Diffusion-Based Model for Semantic Segmentation of Lumbar Spine MRI in Lower Back Pain Patients, Enhanced by Pre-Segmentation with nnU-Net for Accelerated Training
This research introduces SpineSegDiff, a novel diffusion-based model for accurate and efficient segmentation of lumbar spine MRI, demonstrating superior performance in identifying degenerated intervertebral discs, a crucial aspect of low back pain diagnosis and treatment.
Comprehensive Benchmark for Classifying Lymph Node Metastasis Severity in Breast Cancer Images using Multiple Instance Learning
This paper introduces Camelyon+, a refined and expanded dataset based on Camelyon-16 and Camelyon-17, for benchmarking Multiple Instance Learning (MIL) models in classifying the severity of lymph node metastasis in breast cancer images.
基於改進有限預算分配的排名與選擇中正確選擇概率的級數展開
本文提出了一種新的基於Bahadur-Rao類型的級數展開方法，用於在有限樣本情況下逼近排名與選擇（R&S）問題中的正確選擇概率（PCS），並基於此開發了一種新的有限預算分配策略，以提高序數優化的有限樣本性能。
HIST-AID：利用歷史病患報告增強多模態自動診斷
整合歷史病患報告，特別是最近的報告，可以顯著提高胸部X光片異常檢測的準確性。
HIST-AID: 과거 환자 기록을 활용한 향상된 다중 모드 자동 진단
HIST-AID 프레임워크는 과거 흉부 엑스레이 및 방사선 보고서를 활용하여 자동 흉부 엑스레이 이상 감지의 정확성을 향상시킵니다.
HIST-AID：過去の患者レポートを活用した、より強化されたマルチモーダル自動診断
過去のレントゲン画像と診断レポートを統合することで、胸部X線画像の自動診断精度が大幅に向上する。
HIST-AID: Enhancing Multi-Modal Automatic Diagnosis of Thoracic Abnormalities from Chest X-Rays by Leveraging Historical Patient Reports
Integrating historical patient reports with chest X-rays in a temporal multi-modal learning framework significantly improves the accuracy of automatic diagnosis for thoracic abnormalities.
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