While many machine learning methods have been used for medical prediction and risk factor analysis on healthcare data, most prior research has involved single-task learning (STL) methods. However, healthcare research often involves multiple related tasks. For instance, implementation of disease scores prediction and risk factor analysis in multiple subgroups of patients simultaneously and risk factor analysis at multi-levels synchronously. In this paper, we developed a new ensemble machine learning Python package based on multi-task learning (MTL), referred to as the Med-Multi-Task Learning (MD-MTL) package and applied it in predicting disease scores of patients, and in carrying out risk factor analysis on multiple subgroups of patients simultaneously. Our experimental results on two datasets demonstrate the utility of the MD-MTL package, and show the advantage of MTL (vs. STL), when analyzing data that is organized into different categories (tasks, which can be various age groups, different levels of disease severity, etc.).
University of British Columbia（不列颠哥伦比亚大学）；University of Pennsylvania（宾夕法尼亚大学）
Social media currently provide a window on our lives, making it possible to learn how people from different places, with different backgrounds, ages, and genders use language. In this work we exploit a newly-created Arabic dataset with ground truth age and gender labels to learn these attributes both individually and in a multi-task setting at the sentence level. Our models are based on variations of deep bidirectional neural networks. More specifically, we build models with gated recurrent units and bidirectional encoder representations from transformers (BERT). We show the utility of multi-t…
Southern University of Science and Technology（南方科技大学）；Peng Cheng Laboratory（鹏城实验室）
This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi-Task Learning (MTL). LibMTL considers different settings and approaches in MTL, and it supports a large number of state-of-the-art MTL methods, including 12 loss weighting strategies, 7 architectures, and 84 combinations of different architectures and loss weighting methods. Moreover, the modular design in LibMTL makes it easy-to-use and well extensible, thus users can easily and fast develop new MTL methods, compar…
Centre for Vision, Speech and Signal Processing (CVSSP)（视觉、语音与信号处理中心（CVSSP））；University of Surrey（萨里大学）
Recent approaches to multi-task learning (MTL) have focused on modelling connections between tasks at the decoder level. This leads to a tight coupling between tasks, which need retraining if a new task is inserted or removed. We argue that MTL is a stepping stone towards universal feature learning (UFL), which is the ability to learn generic features that can be applied to new tasks without retraining. We propose Medusa to realize this goal, designing task heads with dual attention mechanisms. The shared feature attention masks relevant backbone features for each task, allowing it to learn a …
University of Toronto（多伦多大学）；University Health Network（大学健康网络）；Princess Margaret Cancer Centre（玛格丽特公主癌症中心）
Accurate survival prediction is crucial for development of precision cancer medicine, creating the need for new sources of prognostic information. Recently, there has been significant interest in exploiting routinely collected clinical and medical imaging data to discover new prognostic markers in multiple cancer types. However, most of the previous studies focus on individual data modalities alone and do not make use of recent advances in machine learning for survival prediction. We present Deep-CR MTLR -- a novel machine learning approach for accurate cancer survival prediction from multi-mo…
Predicting multiple heterogeneous biological and medical targets is a challenge for traditional deep learning models. In contrast to single-task learning, in which a separate model is trained for each target, multi-task learning (MTL) optimizes a single model to predict multiple related targets simultaneously. To address this challenge, we propose the Multi-gate Mixture-of-Experts with Exclusivity (MMoEEx). Our work aims to tackle the heterogeneous MTL setting, in which the same model optimizes multiple tasks with different characteristics. Such a scenario can overwhelm current MTL approaches …
Real world multimedia data is often composed of multiple modalities such as an image or a video with associated text (e.g. captions, user comments, etc.) and metadata. Such multimodal data packages are prone to manipulations, where a subset of these modalities can be altered to misrepresent or repurpose data packages, with possible malicious intent. It is, therefore, important to develop methods to assess or verify the integrity of these multimedia packages. Using computer vision and natural language processing methods to directly compare the image (or video) and the associated caption to veri…
Cross-Task Attention Network: Improving Multi-Task Learning for Medical Imaging Applications
University of Toronto（多伦多大学）；Princess Margaret Cancer Centre（玛格丽特公主癌症中心）；University Health Network（大学健康网络）；Princess Margaret Research Institute（玛格丽特公主研究所）；Toronto General Research Institute（多伦多综合研究所）；Peter Munk Cardiac Centre（彼得·蒙克心脏中心）；Vector Institute（向量研究所）
MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors
Rayscape；University of Bucharest（布加勒斯特大学）；Polytechnic University of Timişoara（蒂米什瓦拉工业大学）；Colţea Clinical Hospital（科尔切亚医院）；Carol Davila University of Medicine and Pharmacy（卡罗尔·迪瓦ila医学院和药理学大学）
Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis
Department of Psychiatry, Wonkwang University Hospital（精神医学科，Wonkwang大学医院）；RSC LAB, MODULABS（RSC实验室，MODULABS）；The State University of New York, Korea, Republic of Korea（纽约州立大学，韩国）；Department of Pediatrics, Seoul National University Bundang Hospital（儿科，首尔国立大学医院 Bundang 分院）；Department of Pediatrics, Seoul National University College of Medicine（儿科，首尔国立大学医学院）
Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization
University of Washington（华盛顿大学）；New York University（纽约大学）；Liverpool John Moores University（利物浦约翰·摩尔斯大学）；Birla Institute of Technology Pilani（比拉理工学院帕利尼分校）；Columbia University（哥伦比亚大学）
Shanghai Jiao Tong University（上海交通大学）；Ruijin Hospital, SJTU School of Medicine（上海交通大学医学院附属瑞金医院）；Paradigm Inc.（第四范式（北京）技术有限公司）
Diabetes prediction is an important data science application in the social healthcare domain. There exist two main challenges in the diabetes prediction task: data heterogeneity since demographic and metabolic data are of different types, data insufficiency since the number of diabetes cases in a single medical center is usually limited. To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency. To this end, Task-wise Split Gradient Boosting Trees (TSGB) is proposed for the …
University of Science and Technology of China（中国科学技术大学）
Segmentation of multiple anatomical structures is of great importance in medical image analysis. In this study, we proposed a $\mathcal{W}$-net to simultaneously segment both the optic disc (OD) and the exudates in retinal images based on the multi-task learning (MTL) scheme. We introduced a class-balanced loss and a multi-task weighted loss to alleviate the imbalanced problem and to improve the robustness and generalization property of the $\mathcal{W}$-net. We demonstrated the effectiveness of our approach by applying five-fold cross-validation experiments on two public datasets e\_ophtha\_E…
Medical image classification involves thresholding of labels that represent malignancy risk levels. Usually, a task defines a single threshold, and when developing computer-aided diagnosis tools, a single network is trained per such threshold, e.g. as screening out healthy (very low risk) patients to leave possibly sick ones for further analysis (low threshold), or trying to find malignant cases among those marked as non-risk by the radiologist ("second reading", high threshold). We propose a way to rephrase the classification problem in a manner that yields several problems (corresponding to …
Leiden University Medical Center（莱顿大学医学中心）；Leiden Institute of Advanced Computer Science（莱顿高级计算机科学研究所）
Medical image registration and segmentation are two of the most frequent tasks in medical image analysis. As these tasks are complementary and correlated, it would be beneficial to apply them simultaneously in a joint manner. In this paper, we formulate registration and segmentation as a joint problem via a Multi-Task Learning (MTL) setting, allowing these tasks to leverage their strengths and mitigate their weaknesses through the sharing of beneficial information. We propose to merge these tasks not only on the loss level, but on the architectural level as well. We studied this approach in th…
