Automated Medical Diagnosis with Deep Learning

Automated Medical Diagnosis

Deep Automated Multi-task Learning

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as secondary tasks in an MTL model. We explore next word prediction, next character pr...

Deep Automated Multit-task Learning

Multi-task learning (MTL) has recently contributed to learning better representations in service of various NLP tasks. MTL aims at improving the performance of a primary task, by jointly training on a secondary task. This paper introduces automated tasks, which exploit the sequential nature of the input data, as secondary tasks in an MTL model. We explore next word prediction, next character pr...

Melanoma detection with a deep learning model

Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions.    Methods: In this analytic s...

ADC: Automated Deep Compression and Acceleration with Reinforcement Learning

Model compression is an effective technique facilitating the deployment of neural network models on mobile devices that have limited computation resources and a tight power budget. However, conventional model compression techniques [19, 20, 23] use hand-crafted features and require domain experts to explore the large design space trading off model size, speed, and accuracy, which is usually sub...