One stage lesion detection based on 3D context convolutional neural networks

Cai, Guorong; Chen, Jinshan; Wu, Zebiao; Tang, Haoming; Liu, Yujun; Wang, Senyuan; Su, Songzhi

doi:10.1016/j.compeleceng.2019.106449

Cited by 11 publications

(2 citation statements)

References 10 publications

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“…based on Faster-RCNN [28] or Mask-RCNN [29]. Yet, recent advanced one-stage methods [19], [30], [31], [32], [33] have shown excellent performance while retaining simpler formulations. Among these, CenterNet [19] provides a solution well balanced in terms of Fig.…”

Section: A Related Workmentioning

confidence: 99%

Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning

Huo¹,

Cai²,

Cheng³

et al. 2020

Preprint

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Effective and non-invasive radiological imaging based tumor/lesion characterization (e.g., subtype classification) has long been a major aim in the oncology diagnosis and treatment procedures, with the hope of reducing needs for invasive surgical biopsies. Prior work are generally very restricted to a limited patient sample size, especially using patient studies with confirmed pathological reports as ground truth. In this work, we curate a patient cohort of 1305 dynamic contrast CT studies (i.e., 5220 multi-phase 3D volumes) with pathology confirmed ground truth. A novel fully-automated and multi-stage liver tumor characterization framework is proposed, comprising four steps of tumor proposal detection, tumor harvesting, primary tumor site selection, and deep texture-based characterization. More specifically, (1) we propose a 3D non-isotropic anchor-free lesion detection method; (2) we present and validate the use of multi-phase deep texture learning for precise liver lesion tissue characterization, named spatially adaptive deep texture (SaDT);(3) we leverage small-sized public datasets to semi-automatically curate our large-scale clinical dataset of 1305 patients where four main liver tumor subtypes of primary, secondary, metastasized and benign are presented. Extensive evaluations demonstrate that our new data curation strategy, combined with the SaDT deep dynamic texture analysis, can effectively improve the mean F1 scores by > 8.6% compared with baselines, in differentiating four major liver lesion types. This is a significant step towards the clinical goal.

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Section: A Related Workmentioning

confidence: 99%

Harvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning

Huo¹,

Cai²,

Cheng³

et al. 2020

Preprint

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“…According to the literature 14 and other related literature, 10,11,15 several results have been achieved according to the transfer training process, and transfer learning has also been successfully applied in some fields. [16][17][18][19] As a machine learning method, transfer learning can reuse a model developed for one task in another different task, 20 and this model can be used as the starting point of the model for another task. Depth image is not like the RGB image which has big annotated data sets such as Ima-geNet, 21 and this is also one of the main problems that cause the neural network model unable to conveniently utilize the depth information.…”

Section: Supervision Transfer Learningmentioning

confidence: 99%

A novel detection fusion network for solid waste sorting

Zou

Yin

2020

International Journal of Advanced Robotic Systems

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Vision-based object detection technology plays a very important role in the field of computer vision. It is widely used in many machine vision applications. However, in the specific application scenarios, like a solid waste sorting system, it is very difficult to obtain good accuracy due to the color information of objects that is badly damaged. In this work, we propose a novel multimodal convolutional neural network method for RGB-D solid waste object detection. The depth information is introduced as the new modal to improve the object detection performance. Our method fuses two individual features in multiple scales, which forms an end-to-end network. We evaluate our method on the self-constructed solid waste data set. In comparison with single modal detection and other popular cross modal fusion neural networks, our method achieves remarkable results with high validity, reliability, and real-time detection speed.

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