129 citations · 305 across the 18 of their papers we have counts for
8 papers · 1 filter
When Prompt-based Incremental Learning Does Not Meet Strong Pretraining
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng
Incremental learning aims to overcome catastrophic forgetting when learning deep networks from sequential tasks. With impressive learning efficiency and performance, prompt-based m…
A scoping review on multimodal deep learning in biomedical images and texts
Zhaoyi Sun, Mingquan Lin, Qingqing Zhu +4
Computer-assisted diagnostic and prognostic systems of the future should be capable of simultaneously processing multimodal data. Multimodal deep learning (MDL), which involves the…
RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging
Ajay Jaiswal, Kumar Ashutosh, Justin F Rousseau +3
AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-q…
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data
Yu-Ming Tang, Yi-Xing Peng, Wei-Shi Zheng
Deep neural network (DNN) suffers from catastrophic forgetting when learning incrementally, which greatly limits its applications. Although maintaining a handful of samples (called…
MULAN: Multitask Universal Lesion Analysis Network for Joint Lesion Detection, Tagging, and Segmentation
Ke Yan, Youbao Tang, Yifan Peng +4
When reading medical images such as a computed tomography (CT) scan, radiologists generally search across the image to find lesions, characterize and measure them, and then describ…
Holistic and Comprehensive Annotation of Clinically Significant Findings on Diverse CT Images: Learning from Radiology Reports and Label Ontology
Ke Yan, Yifan Peng, Veit Sandfort +3
In radiologists' routine work, one major task is to read a medical image, e.g., a CT scan, find significant lesions, and describe them in the radiology report. In this paper, we st…