ChrEn: Cherokee-English Machine Translation for Endangered Language Revitalization

1 code implementation • EMNLP 2020 • Shiyue Zhang, Benjamin Frey, Mohit Bansal

To help save this endangered language, we introduce ChrEn, a Cherokee-English parallel dataset, to facilitate machine translation research between Cherokee and English.

Cultural Vocal Bursts Intensity Prediction Language Modelling +5

ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback

2 code implementations • ACL 2021 • Shiyue Zhang, Benjamin Frey, Mohit Bansal

The quantitative evaluation demonstrates that our backbone translation models achieve state-of-the-art translation performance and our quality estimation well correlates with both BLEU and human judgment.

Machine Translation NMT +3

Deep learning for brain metastasis detection and segmentation in longitudinal MRI data

1 code implementation • 22 Dec 2021 • Yixing Huang, Christoph Bert, Philipp Sommer, Benjamin Frey, Udo Gaipl, Luitpold V. Distel, Thomas Weissmann, Michael Uder, Manuel A. Schmidt, Arnd Dörfler, Andreas Maier, Rainer Fietkau, Florian Putz

To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels.

Ensemble Learning Specificity

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy

no code implementations • 28 Aug 2022 • Thomas Weissmann, Yixing Huang, Stefan Fischer, Johannes Roesch, Sina Mansoorian, Horacio Ayala Gaona, Antoniu-Oreste Gostian, Markus Hecht, Sebastian Lettmaier, Lisa Deloch, Benjamin Frey, Udo S. Gaipl, Luitpold V. Distel, Andreas Maier, Heinrich Iro, Sabine Semrau, Christoph Bert, Rainer Fietkau, Florian Putz

For a subgroup of 10 cases, intraobserver variability was compared to the average DL autosegmentation accuracy on the original and recontoured set of expert segmentations.

The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning

no code implementations • 16 Apr 2023 • Florian Putz, Johanna Grigo, Thomas Weissmann, Philipp Schubert, Daniel Hoefler, Ahmed Gomaa, Hassen Ben Tkhayat, Amr Hagag, Sebastian Lettmaier, Benjamin Frey, Udo S. Gaipl, Luitpold V. Distel, Sabine Semrau, Christoph Bert, Rainer Fietkau, Yixing Huang

Conclusions: The Segment Anything foundation model, while trained on photos, can achieve high zero-shot accuracy for glioma brain tumor segmentation on MRI slices.

Brain Tumor Segmentation Segmentation +1

Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology

1 code implementation • 24 Apr 2023 • Yixing Huang, Ahmed Gomaa, Sabine Semrau, Marlen Haderlein, Sebastian Lettmaier, Thomas Weissmann, Johanna Grigo, Hassen Ben Tkhayat, Benjamin Frey, Udo S. Gaipl, Luitpold V. Distel, Andreas Maier, Rainer Fietkau, Christoph Bert, Florian Putz

The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam.

Benchmarking Decision Making +1