A survey on deep learning in medical image analysis

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions Next, the performance, speed, and properties of deep learning approaches are summarized and discussed Finally, we provide a critical assessment of the current state and identify likely future developments and trends

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Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study.

Task-oriented dialogue is often decomposed into three tasks: understanding user input, deciding actions, and generating a response. While such decomposition might suggest a dedicated model for each sub-task, we find a simple, unified approach leads to state-of-the-art performance on the MultiWOZ dataset. SimpleTOD is a simple approach to task-oriented dialogue that uses a single causal language model trained on all sub-tasks recast as a single sequence prediction problem. This allows SimpleTOD to fully leverage transfer learning from pre-trained, open domain, causal language models such as GPT-2. SimpleTOD improves over the prior state-of-the-art by 0.49 points in joint goal accuracy for dialogue state tracking. More impressively, SimpleTOD also improves the main metrics used to evaluate action decisions and response generation in an end-to-end setting for task-oriented dialog systems: inform rate by 8.1 points, success rate by 9.7 points, and combined score by 7.2 points.

A Simple Language Model for Task-Oriented Dialogue

Pre-training models have been proved effective for a wide range of natural language processing tasks. Inspired by this, we propose a novel dialogue generation pre-training framework to support various kinds of conversations, including chit-chat, knowledge grounded dialogues, and conversational question answering. In this framework, we adopt flexible attention mechanisms to fully leverage the bi-directional context and the uni-directional characteristic of language generation. We also introduce discrete latent variables to tackle the inherent one-to-many mapping problem in response generation. Two reciprocal tasks of response generation and latent act recognition are designed and carried out simultaneously within a shared network. Comprehensive experiments on three publicly available datasets verify the effectiveness and superiority of the proposed framework.

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PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable

To build a high-quality open-domain chatbot, we introduce the effective training process of PLATO-2 via curriculum learning. There are two stages involved in the learning process. In the first stage, a coarse-grained generation model is trained to learn response generation under the simplified framework of one-to-one mapping. In the second stage, a fine-grained generative model augmented with latent variables and an evaluation model are further trained to generate diverse responses and to select the best response, respectively. PLATO-2 was trained on both Chinese and English data, whose effectiveness and superiority are verified through comprehensive evaluations, achieving new state-of-the-art results.

PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning

In this paper, a novel method for brain MR image segmentation has been proposed, with deep learning techniques to obtain preliminary labelling and graphical models to produce the final result. A specific architecture, namely multi-scale structured convolutional neural networks (MS-CNN), is designed to capture discriminative features for each sub-cortical structure and to generate a label probability map for the target image. Due to complex background in brain images and the lack of spatial constraints among testing samples, the initial result obtained with MS-CNN is not smooth. To deal with this problem, dynamic random walker with decayed region of interest is then proposed to enforce label consistency. Comprehensive evaluations have been carried out on two publicly available data-sets and experimental results indicate that the proposed method can obtain better segmentation quality efficiently.

Multi-scale structured CNN with label consistency for brain MR image segmentation

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Siqi Bao

Papers

PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable

PLATO: Pre-trained Dialogue Generation Model with Discrete Latent Variable

PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning

Multi-scale structured CNN with label consistency for brain MR image segmentation

PLATO-2: Towards Building an Open-Domain Chatbot via Curriculum Learning