Multi-task Learning for Fine-Grained Eye Disease Prediction

2020 ◽  
pp. 734-749
Author(s):  
Sahil Chelaramani ◽  
Manish Gupta ◽  
Vipul Agarwal ◽  
Prashant Gupta ◽  
Ranya Habash
Keyword(s):  
Eye Disease ◽  
Disease Prediction ◽  
Fine Grained ◽  
Task Learning

Attention-Based Multi-Task Learning For Fine-Grained Image Classification

2021 ◽  
Author(s):  
Dichao Liu ◽  
Yu Wang ◽  
Kenji Mase ◽  
Jien Kato
Keyword(s):  
Image Classification ◽  
Fine Grained ◽  
Task Learning

scholarly journals Multi-Task Learning for Diabetic Retinopathy Grading and Lesion Segmentation

2020 ◽  
Vol 34 (08) ◽  
pp. 13267-13272
Author(s):  
Alex Foo ◽  
Wynne Hsu ◽  
Mong Li Lee ◽  
Gilbert Lim ◽  
Tien Yin Wong
Keyword(s):  
Diabetic Retinopathy ◽  
State Of The Art ◽  
Great Success ◽  
Automated Segmentation ◽  
Lesion Segmentation ◽  
Severity Level ◽  
Fine Grained ◽  
Acceptable Accuracy ◽  
Task Learning ◽  
Different Types

Although deep learning for Diabetic Retinopathy (DR) screening has shown great success in achieving clinically acceptable accuracy for referable versus non-referable DR, there remains a need to provide more fine-grained grading of the DR severity level as well as automated segmentation of lesions (if any) in the retina images. We observe that the DR severity level of an image is dependent on the presence of different types of lesions and their prevalence. In this work, we adopt a multi-task learning approach to perform the DR grading and lesion segmentation tasks. In light of the lack of lesion segmentation mask ground-truths, we further propose a semi-supervised learning process to obtain the segmentation masks for the various datasets. Experiments results on publicly available datasets and a real world dataset obtained from population screening demonstrate the effectiveness of the multi-task solution over state-of-the-art networks.

Dual feature correlation guided multi-task learning for Alzheimer's disease prediction

2021 ◽  
pp. 105090
Author(s):  
Shanshan Tang ◽  
Peng Cao ◽  
Min Hang ◽  
Xiaoli Liu ◽  
Osmar Zaiane
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Disease Prediction ◽  
Task Learning ◽  
Feature Correlation

Jointly Predicting Future Content in Multiple Social Media Sites Based on Multi-task Learning

2022 ◽  
Vol 40 (4) ◽  
pp. 1-28
Author(s):  
Peng Zhang ◽  
Baoxi Liu ◽  
Tun Lu ◽  
Xianghua Ding ◽  
Hansu Gu ◽  
...  
Keyword(s):  
Social Media ◽  
User Behavior ◽  
Learning Method ◽  
Data Sampling ◽  
Behavior Prediction ◽  
Fine Grained ◽  
Task Learning ◽  
Social Media Site ◽  
Site User ◽  
Cross Site

User-generated contents (UGC) in social media are the direct expression of users’ interests, preferences, and opinions. User behavior prediction based on UGC has increasingly been investigated in recent years. Compared to learning a person’s behavioral patterns in each social media site separately, jointly predicting user behavior in multiple social media sites and complementing each other (cross-site user behavior prediction) can be more accurate. However, cross-site user behavior prediction based on UGC is a challenging task due to the difficulty of cross-site data sampling, the complexity of UGC modeling, and uncertainty of knowledge sharing among different sites. For these problems, we propose a Cross-Site Multi-Task (CSMT) learning method to jointly predict user behavior in multiple social media sites. CSMT mainly derives from the hierarchical attention network and multi-task learning. Using this method, the UGC in each social media site can obtain fine-grained representations in terms of words, topics, posts, hashtags, and time slices as well as the relevances among them, and prediction tasks in different social media sites can be jointly implemented and complement each other. By utilizing two cross-site datasets sampled from Weibo, Douban, Facebook, and Twitter, we validate our method’s superiority on several classification metrics compared with existing related methods.

Semi-supervised Multi-task Learning for Multi-label Fine-grained Sexism Classification

2020 ◽  
Author(s):  
Harika Abburi ◽  
Pulkit Parikh ◽  
Niyati Chhaya ◽  
Vasudeva Varma
Keyword(s):  
Fine Grained ◽  
Task Learning

Attribute hierarchy based multi-task learning for fine-grained image classification

2020 ◽  
Vol 395 ◽  
pp. 150-159 ◽  
Author(s):  
Junjie Zhao ◽  
Yuxin Peng ◽  
Xiangteng He
Keyword(s):  
Image Classification ◽  
Fine Grained ◽  
Task Learning ◽  
Attribute Hierarchy

scholarly journals Using behavioral rhythms and multi-task learning to predict fine-grained symptoms of schizophrenia

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vincent W.-S. Tseng ◽  
Akane Sano ◽  
Dror Ben-Zeev ◽  
Rachel Brian ◽  
Andrew T. Campbell ◽  
...  
Keyword(s):  
Hearing Voices ◽  
Assessment Scores ◽  
Behavioral Rhythms ◽  
Fine Grained ◽  
Task Learning ◽  
Extra Effort ◽  
Symptom Trajectories ◽  
Behavioral Rhythm ◽  
Interpretable Models ◽  
Symptom Changes

Abstract Schizophrenia is a severe and complex psychiatric disorder with heterogeneous and dynamic multi-dimensional symptoms. Behavioral rhythms, such as sleep rhythm, are usually disrupted in people with schizophrenia. As such, behavioral rhythm sensing with smartphones and machine learning can help better understand and predict their symptoms. Our goal is to predict fine-grained symptom changes with interpretable models. We computed rhythm-based features from 61 participants with 6,132 days of data and used multi-task learning to predict their ecological momentary assessment scores for 10 different symptom items. By taking into account both the similarities and differences between different participants and symptoms, our multi-task learning models perform statistically significantly better than the models trained with single-task learning for predicting patients’ individual symptom trajectories, such as feeling depressed, social, and calm and hearing voices. We also found different subtypes for each of the symptoms by applying unsupervised clustering to the feature weights in the models. Taken together, compared to the features used in the previous studies, our rhythm features not only improved models’ prediction accuracy but also provided better interpretability for how patients’ behavioral rhythms and the rhythms of their environments influence their symptom conditions. This will enable both the patients and clinicians to monitor how these factors affect a patient’s condition and how to mitigate the influence of these factors. As such, we envision that our solution allows early detection and early intervention before a patient’s condition starts deteriorating without requiring extra effort from patients and clinicians.

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