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Found 10 papers, 2 papers with code
Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis
For a clinical image, our model generates three outputs: a hierarchical prediction, an alert for out-of-distribution images, and a recommendation for dermoscopy if clinical image alone is insufficient for diagnosis.
Privacy-preserving Early Detection of Epileptic Seizures in Videos
In this work, we contribute towards the development of video-based epileptic seizure classification by introducing a novel framework (SETR-PKD), which could achieve privacy-preserved early detection of seizures in videos.
TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classification
Due to the high resolution of the WSI and the unavailability of patch-level annotations, WSI classification is usually formulated as a weakly supervised problem, which relies on multiple instance learning (MIL) based on patches of a WSI.
Multi-dimensional Racism Classification during COVID-19: Stigmatization, Offensiveness, Blame, and Exclusion
With the aid of BERT and topic modeling, this categorical detection enables insights into the underlying subtlety of racist discussion on digital platforms during COVID-19.
Leukocyte Classification using Multimodal Architecture Enhanced by Knowledge Distillation
Our unique contribution is two-fold - 1) We present a first of its kind multimodal WBC dataset for WBC classification; 2) We develop a high performing multimodal architecture which is also efficient and low in complexity at the same time.
Out-of-Distribution Detection for Long-tailed and Fine-grained Skin Lesion Images
Through this approach, 1) First, we target the mixup amongst middle and tail classes to address the long-tail problem.
Beyond a binary of (non)racist tweets: A four-dimensional categorical detection and analysis of racist and xenophobic opinions on Twitter in early Covid-19
Transcending the binary categorization of racist and xenophobic texts, this research takes cues from social science theories to develop a four dimensional category for racism and xenophobia detection, namely stigmatization, offensiveness, blame, and exclusion.
Towards Automated and Marker-less Parkinson Disease Assessment: Predicting UPDRS Scores using Sit-stand videos
For BRADY we find F1-scores of 0. 75 using our framework compared to 0. 50 for the video based rater clinicians, while for PIGD we find 0. 78 for the framework and 0. 45 for the video based rater clinicians.
DeepActsNet: Spatial and Motion features from Face, Hands, and Body Combined with Convolutional and Graph Networks for Improved Action Recognition
Existing action recognition methods mainly focus on joint and bone information in human body skeleton data due to its robustness to complex backgrounds and dynamic characteristics of the environments.
#Coronavirus or #Chinesevirus?!: Understanding the negative sentiment reflected in Tweets with racist hashtags across the development of COVID-19
Situated in the global outbreak of COVID-19, our study enriches the discussion concerning the emergent racism and xenophobia on social media.
