A Cascaded Learning Strategy for Robust COVID-19 Pneumonia Chest X-Ray Screening

no code implementations • 24 Apr 2020 • Chun-Fu Yeh, Hsien-Tzu Cheng, Andy Wei, Hsin-Ming Chen, Po-Chen Kuo, Keng-Chi Liu, Mong-Chi Ko, Ray-Jade Chen, Po-Chang Lee, Jen-Hsiang Chuang, Chi-Mai Chen, Yi-Chang Chen, Wen-Jeng Lee, Ning Chien, Jo-Yu Chen, Yu-Sen Huang, Yu-Chien Chang, Yu-Cheng Huang, Nai-Kuan Chou, Kuan-Hua Chao, Yi-Chin Tu, Yeun-Chung Chang, Tyng-Luh Liu

We introduce a comprehensive screening platform for the COVID-19 (a. k. a., SARS-CoV-2) pneumonia.

Classification General Classification +1

Self-similarity Student for Partial Label Histopathology Image Segmentation

no code implementations • ECCV 2020 • Hsien-Tzu Cheng, Chun-Fu Yeh, Po-Chen Kuo, Andy Wei, Keng-Chi Liu, Mong-Chi Ko, Kuan-Hua Chao, Yu-Ching Peng, Tyng-Luh Liu

Following this similarity learning, our similarity ensemble merges similar patches' ensembled predictions as the pseudo-label of a given patch to counteract its noisy label.

Image Segmentation Pseudo Label +2

AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model

no code implementations • 27 Sep 2023 • Seungwhan Moon, Andrea Madotto, Zhaojiang Lin, Tushar Nagarajan, Matt Smith, Shashank Jain, Chun-Fu Yeh, Prakash Murugesan, Peyman Heidari, Yue Liu, Kavya Srinet, Babak Damavandi, Anuj Kumar

We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i. e. text, image, video, audio, IMU motion sensor), and generates textual responses.

Language Modelling Video Question Answering