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Weakly Supervised Medical Diagnosis and Localization from Multiple Resolutions [article]

Li Yao, Jordan Prosky, Eric Poblenz, Ben Covington, Kevin Lyman
2018 arXiv   pre-print
We introduce an approach to managing these practical constraints by applying a novel architecture which learns at multiple resolutions while generating saliency maps with weak supervision.  ...  Applying this approach to interpreting chest x-rays, we set the state of the art on 9 abnormalities in the NIH's CXR14 dataset while generating saliency maps with the highest resolution to date.  ...  Figure 1 illustrates the importance of high-resolution saliency maps for reliable clinical interpretation. Fig. 1 : Chest X-ray with saliency maps of increasing resolutions.  ... 
arXiv:1803.07703v1 fatcat:imw2fo7p7vfnfejvvhxpnf7juy

Manifold-driven Attention Maps for Weakly Supervised Segmentation [article]

Sukesh Adiga V, Jose Dolz, Herve Lombaert
2020 arXiv   pre-print
To mitigate this problem, weakly supervised learning has emerged as an efficient alternative, which employs image-level labels, scribbles, points, or bounding boxes as supervision.  ...  Our method generates superior attention maps directly during inference without the need of extra computations.  ...  of a GPU.  ... 
arXiv:2004.03046v1 fatcat:cla7kqcdojbspbl6igue2uj4gy

Weakly Supervised Object Localization and Detection: A Survey [article]

Dingwen Zhang, Junwei Han, Gong Cheng, Ming-Hsuan Yang
2021 arXiv   pre-print
As an emerging and challenging problem in the computer vision community, weakly supervised object localization and detection plays an important role for developing new generation computer vision systems  ...  applications of the weakly supervised object localization and detection methods, and potential future directions to further promote the development of this research field.  ...  Fig. 2 shows our taxonomy of the studies in the research field of weakly supervised object localization and In the left block, taxonomy of the existing approaches for weakly supervised object localization  ... 
arXiv:2104.07918v1 fatcat:dwl6sjfzibdilnvjnrbifp4uke

A Survey on Deep Learning of Small Sample in Biomedical Image Analysis [article]

Pengyi Zhang, Yunxin Zhong, Yulin Deng, Xiaoying Tang, Xiaoqiong Li
2019 arXiv   pre-print
We survey the key SSL techniques by dividing them into five categories: (1) explanation techniques, (2) weakly supervised learning techniques, (3) transfer learning techniques, (4) active learning techniques  ...  The success of deep learning has been witnessed as a promising technique for computer-aided biomedical image analysis, due to end-to-end learning framework and availability of large-scale labelled samples  ...  Acknowledgements The authors would like to thank members of the Medical Image Analysis for discussions and suggestions.  ... 
arXiv:1908.00473v1 fatcat:atotvdxp6janve2mz3swyf47xa

TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency [article]

Lin Cheng, Pengfei Fang, Yanjie Liang, Liao Zhang, Chunhua Shen, Hanzi Wang
2022 arXiv   pre-print
The proposed TSGB consists of two components, namely, TSGB-Conv and TSGB-FC, which rectify the gradients for convolutional layers and fully-connected layers, respectively.  ...  and further efficiently propagate the saliency to the image space, thereby generating target-selective and fine-grained saliency maps.  ...  Weakly-Supervised Localization 1) Object Localization: A satisfactory saliency method is expected to generate target-relevant saliency maps, where the areas with high intensity indicate the positions of  ... 
arXiv:2110.05182v2 fatcat:4pdtc6nervcefe4hmtyrqdauem

Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus [article]

Sangjoon Park, Gwanghyun Kim, Yujin Oh, Joon Beom Seo, Sang Min Lee, Jin Hwan Kim, Sungjun Moon, Jae-Kwang Lim, Jong Chul Ye
2021 arXiv   pre-print
Under the global COVID-19 crisis, developing robust diagnosis algorithm for COVID-19 using CXR is hampered by the lack of the well-curated COVID-19 data set, although CXR data with other disease are abundant  ...  However, the direct use of existing vision transformer that uses the corpus generated by the ResNet is not optimal for correct feature embedding.  ...  Fig. 2 illustrates the examples of visualization of saliency map for each disease classes.  ... 
arXiv:2103.07055v1 fatcat:jlglqihlpzbzfgojuvwepbqpwm

Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Comparative Study [article]

Jérôme Rony, Soufiane Belharbi, Jose Dolz, Ismail Ben Ayed, Luke McCaffrey, Eric Granger
2022 arXiv   pre-print
Deep weakly-supervised object localization (WSOL) methods provide different strategies for low-cost training of deep learning models.  ...  Using only image-class annotations, these methods can be trained to classify an image, and yield class activation maps (CAMs) for ROI localization.  ...  Weakly supervised object localization framework provides different techniques for lowcost training of deep models.  ... 
arXiv:1909.03354v5 fatcat:cbkan6dnl5ctdankkv4tblso6e

Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images [article]

Qinghao Ye, Yuan Gao, Weiping Ding, Zhangming Niu, Chengjia Wang, Yinghui Jiang, Minhao Wang, Evandro Fei Fang, Wade Menpes-Smith, Jun Xia, Guang Yang
2021 arXiv   pre-print
supervised learning paradigm.  ...  The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2  ...  Yang, Weakly supervised ob- ject localization with progressive domain adaptation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 3512–3520.  ... 
arXiv:2112.04984v1 fatcat:spnk3ztuevcavgaje6acjp4ula

Visual Feature Attribution Using Wasserstein GANs

Christian F. Baumgartner, Lisa M. Koch, Kerem Can Tezcan, Jia Xi Ang
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
For AD patients the method produces compellingly realistic disease effect maps which are very close to the observed effects.  ...  Attributing the pixels of an input image to a certain category is an important and well-studied problem in computer vision, with applications ranging from weakly supervised localisation to understanding  ...  Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation with the donation of a Titan Xp GPU.  ... 
doi:10.1109/cvpr.2018.00867 dblp:conf/cvpr/BaumgartnerKTAK18 fatcat:ljdvlarpz5bihfndozb4a2tjqu

Use of neuroanatomical pattern regression to predict the structural brain dynamics of vulnerability and transition to psychosis

Nikolaos Koutsouleris, Christian Gaser, Ronald Bottlender, Christos Davatzikos, Petra Decker, Markus Jäger, Gisela Schmitt, Maximilian Reiser, Hans-Jürgen Möller, Eva M. Meisenzahl
2010 Schizophrenia Research  
Pattern regression techniques may facilitate an accurate prediction of these structural brain dynamics, potentially allowing for an early recognition of individuals at risk of developing psychosisassociated  ...  The at-risk mental state for psychosis (ARMS) has been associated with abnormal structural brain dynamics underlying disease transition or non-transition.  ...  Reinhold Bader, Linux Cluster Systems for the Munich and Bavarian Universities, for his support in integrating the machine learning algorithms into the batch system of the Linux cluster.  ... 
doi:10.1016/j.schres.2010.08.032 pmid:20850276 fatcat:2iay6i53cjdkllpmm4izmmdiwy

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Monica Micucci, Antonio Iula
2022 Electronics  
) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported.  ...  The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/electronics11111800 fatcat:htw3q5kednhkbndgk7vw3tbvya

Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models [article]

Dong Wang, Yuewei Yang, Chenyang Tao, Zhe Gan, Liqun Chen, Fanjie Kong, Ricardo Henao, Lawrence Carin
2021 arXiv   pre-print
Further, our causally trained saliency maps are more succinct and meaningful relative to their non-causal counterparts.  ...  We also devise a novel causally informed salience mapping module to identify key image pixels to intervene, and show it greatly facilitates model interpretability.  ...  Weakly-supervised image segmentation. In Figure 6 , we compare saliency maps generated by GradCAM, WBP, WBP (box) to the ground truth lesion masks from expert annotations.  ... 
arXiv:2012.03369v2 fatcat:bjioui6mnfhsjaudiny5vhcxg4

Learning to detect chest radiographs containing pulmonary lesions using visual attention networks

Emanuele Pesce, Samuel Joseph Withey, Petros-Pavlos Ypsilantis, Robert Bakewell, Vicky Goh, Giovanni Montana
2019 Medical Image Analysis  
The first architecture extracts saliency maps from high-level convolutional layers and compares the inferred position of a lesion against the true position when this information is available; a localisation  ...  Both architectures make use of a large number of weakly-labelled images combined with a smaller number of manually annotated x-rays.  ...  Acknowledgments The authors acknowledge the support from the Department of  ... 
doi:10.1016/j.media.2018.12.007 pmid:30660946 fatcat:woy22gopgvbkdkqfw3ntxiypku

A Survey on Contemporary Computer-Aided Tumor, Polyp, and Ulcer Detection Methods in Wireless Capsule Endoscopy Imaging [article]

Tariq Rahim, Muhammad Arslan Usman, Soo Young Shin
2019 arXiv   pre-print
This paper also includes a potential proposal for joint classification of aforementioned three diseases.  ...  Furthermore bleeding inside the GI tract may be the symptoms of these diseases; so an attempt is also made to enlighten the research work done for bleeding detection inside WCE.  ...  For the textural saliency map features a local binary pattern (LBP) is implemented followed by the fusion process of both maps.  ... 
arXiv:1910.00265v1 fatcat:cziq6sauuzaqpgpmca5pmgdwke

Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks [article]

Florian Dubost, Hieab Adams, Pinar Yilmaz, Gerda Bortsova, Gijs van Tulder, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
2020 arXiv   pre-print
For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification  ...  We propose a new weakly supervised detection method using neural networks, that computes attention maps revealing the locations of brain lesions.  ...  This research was funded by The Netherlands Organisation for Health Research and Development (ZonMw) Project 104003005, with additional support of Netherlands Organisation for Scientific Research (NWO)  ... 
arXiv:1906.01891v4 fatcat:xzjplmpj6zamnpuyfk6q2porda
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