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  1. Book ; Online: Importance Estimation with Random Gradient for Neural Network Pruning

    Sapkota, Suman / Bhattarai, Binod

    2023  

    Abstract: Global Neuron Importance Estimation is used to prune neural networks for efficiency reasons. To determine the global importance of each neuron or convolutional kernel, most of the existing methods either use activation or gradient information or both, ... ...

    Abstract Global Neuron Importance Estimation is used to prune neural networks for efficiency reasons. To determine the global importance of each neuron or convolutional kernel, most of the existing methods either use activation or gradient information or both, which demands abundant labelled examples. In this work, we use heuristics to derive importance estimation similar to Taylor First Order (TaylorFO) approximation based methods. We name our methods TaylorFO-abs and TaylorFO-sq. We propose two additional methods to improve these importance estimation methods. Firstly, we propagate random gradients from the last layer of a network, thus avoiding the need for labelled examples. Secondly, we normalize the gradient magnitude of the last layer output before propagating, which allows all examples to contribute similarly to the importance score. Our methods with additional techniques perform better than previous methods when tested on ResNet and VGG architectures on CIFAR-100 and STL-10 datasets. Furthermore, our method also complements the existing methods and improves their performances when combined with them.

    Comment: 7 pages, 2 figures, ICLR 2023 Workshop on Sparsity in Neural Networks. arXiv admin note: text overlap with arXiv:2306.13203
    Keywords Computer Science - Machine Learning
    Subject code 510
    Publishing date 2023-10-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Dimension Mixer

    Sapkota, Suman / Bhattarai, Binod

    A Generalized Method for Structured Sparsity in Deep Neural Networks

    2023  

    Abstract: The recent success of multiple neural architectures like CNNs, Transformers, and MLP-Mixers motivated us to look for similarities and differences between them. We found that these architectures can be interpreted through the lens of a general concept of ... ...

    Abstract The recent success of multiple neural architectures like CNNs, Transformers, and MLP-Mixers motivated us to look for similarities and differences between them. We found that these architectures can be interpreted through the lens of a general concept of dimension mixing. Research on coupling flows and the butterfly transform shows that partial and hierarchical signal mixing schemes are sufficient for efficient and expressive function approximation. In this work, we study group-wise sparse, non-linear, multi-layered and learnable mixing schemes of inputs and find that they are complementary to many standard neural architectures. Following our observations and drawing inspiration from the Fast Fourier Transform, we generalize Butterfly Structure to use non-linear mixer function allowing for MLP as mixing function called Butterfly MLP. We were also able to mix along sequence dimension for Transformer-based architectures called Butterfly Attention. Experiments on CIFAR and LRA datasets demonstrate that the proposed Non-Linear Butterfly Mixers are efficient and scale well when the host architectures are used as mixing function. Additionally, we propose Patch-Only MLP-Mixer for processing spatial 2D signals demonstrating a different dimension mixing strategy.

    Comment: 11 pages, 4 figures, 7 tables
    Keywords Computer Science - Machine Learning
    Subject code 004
    Publishing date 2023-11-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: How does self-supervised pretraining improve robustness against noisy labels across various medical image classification datasets?

    Khanal, Bidur / Bhattarai, Binod / Khanal, Bishesh / Linte, Cristian

    2024  

    Abstract: Noisy labels can significantly impact medical image classification, particularly in deep learning, by corrupting learned features. Self-supervised pretraining, which doesn't rely on labeled data, can enhance robustness against noisy labels. However, this ...

    Abstract Noisy labels can significantly impact medical image classification, particularly in deep learning, by corrupting learned features. Self-supervised pretraining, which doesn't rely on labeled data, can enhance robustness against noisy labels. However, this robustness varies based on factors like the number of classes, dataset complexity, and training size. In medical images, subtle inter-class differences and modality-specific characteristics add complexity. Previous research hasn't comprehensively explored the interplay between self-supervised learning and robustness against noisy labels in medical image classification, considering all these factors. In this study, we address three key questions: i) How does label noise impact various medical image classification datasets? ii) Which types of medical image datasets are more challenging to learn and more affected by label noise? iii) How do different self-supervised pretraining methods enhance robustness across various medical image datasets? Our results show that DermNet, among five datasets (Fetal plane, DermNet, COVID-DU-Ex, MURA, NCT-CRC-HE-100K), is the most challenging but exhibits greater robustness against noisy labels. Additionally, contrastive learning stands out among the eight self-supervised methods as the most effective approach to enhance robustness against noisy labels.
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2024-01-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Clinical characteristics and radiological domains among patients with recurrent strokes-a descriptive cross-sectional study from a tertiary care center in central Nepal.

    Bhattarai, Binod / Sah, Shashi Bhushan

    F1000Research

    2021  Volume 10, Page(s) 757

    Abstract: Background: Stroke is a significant global health hazard that ripples continuum multi-spectral effects to the patients as well their caretakers.   Methods: We studied 28 consecutive cohorts of patients with recurrent strokes managed in our centre within ...

    Abstract Background: Stroke is a significant global health hazard that ripples continuum multi-spectral effects to the patients as well their caretakers.   Methods: We studied 28 consecutive cohorts of patients with recurrent strokes managed in our centre within the last two years.  Results: The most common recurrence stroke pattern was of that of hemorrhagic to hemorrhagic subtype observed in 50% of the patients. The most common anatomical region of involvement was cortical - cortical seen in 39.28% of our cohorts. The surgical intervention was required in 17.85% whereas 42.85% of them were managed conservatively. Paradoxically, 39.28% of patients left against medical advice. The receiver operating curve (ROC) predicting mode of management was highest (area under the curve (AUC) =0.635) for compliance to therapy followed by stroke territory (AUC=0.578), age (AUC=0.457) and motor grading (AUC=0.374). The receiver operating curve (ROC) for influencing decision to leave against medical advice was highest (area under the curve (AUC) =0.861) for motor score followed by sex (AUC=0.701) and age (AUC=0.564). The analysis of variance (ANOVA) study pertaining to the mode of management was significantly connoted by the motor score and the stroke territory only. The ANOVA study pertaining to the decision to leave against medical advice was significantly governed by the motor score, stroke territory, and sex respectively. The multivariate analysis for variables governing mode of management was significant for motor score and the stroke territory only. The multivariate analysis for variables governing leave against medical advice was significant for sex, motor score and the stroke territory.  Conclusions: This study aims to appraise early dichotomization of high-risk patients for recurrent strokes to reduce the continuum of neurological events as well as to mitigate the financial aspects governing stroke care.
    MeSH term(s) Area Under Curve ; Cross-Sectional Studies ; Humans ; Nepal ; Stroke/therapy ; Tertiary Care Centers
    Language English
    Publishing date 2021-08-05
    Publishing country England
    Document type Journal Article
    ZDB-ID 2699932-8
    ISSN 2046-1402 ; 2046-1402
    ISSN (online) 2046-1402
    ISSN 2046-1402
    DOI 10.12688/f1000research.54981.1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: A Near-Complete Sagittal Split of the Adult Cranial Vault due to Traumatic Diastatic Fracture Overlying the Superior Sagittal Sinus.

    Munakomi, Sunil / Bhattarai, Binod / Shah, Sashi B

    Neurology India

    2022  Volume 70, Issue 1, Page(s) 167

    MeSH term(s) Adult ; Cranial Sinuses ; Humans ; Skull ; Superior Sagittal Sinus/diagnostic imaging
    Language English
    Publishing date 2022-03-09
    Publishing country India
    Document type Journal Article
    ZDB-ID 415522-1
    ISSN 1998-4022 ; 0028-3886
    ISSN (online) 1998-4022
    ISSN 0028-3886
    DOI 10.4103/0028-3886.338702
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 698: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Book ; Online: Noisy Heuristics NAS

    Sapkota, Suman / Bhattarai, Binod

    A Network Morphism based Neural Architecture Search using Heuristics

    2022  

    Abstract: Network Morphism based Neural Architecture Search (NAS) is one of the most efficient methods, however, knowing where and when to add new neurons or remove dis-functional ones is generally left to black-box Reinforcement Learning models. In this paper, we ...

    Abstract Network Morphism based Neural Architecture Search (NAS) is one of the most efficient methods, however, knowing where and when to add new neurons or remove dis-functional ones is generally left to black-box Reinforcement Learning models. In this paper, we present a new Network Morphism based NAS called Noisy Heuristics NAS which uses heuristics learned from manually developing neural network models and inspired by biological neuronal dynamics. Firstly, we add new neurons randomly and prune away some to select only the best fitting neurons. Secondly, we control the number of layers in the network using the relationship of hidden units to the number of input-output connections. Our method can increase or decrease the capacity or non-linearity of models online which is specified with a few meta-parameters by the user. Our method generalizes both on toy datasets and on real-world data sets such as MNIST, CIFAR-10, and CIFAR-100. The performance is comparable to the hand-engineered architecture ResNet-18 with the similar parameters.

    Comment: 11 pages, 10 figures, DyNN workshop at the 39 th International Conference on Machine Learning, 2022
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Neural and Evolutionary Computing
    Subject code 006
    Publishing date 2022-07-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Task-guided Domain Gap Reduction for Monocular Depth Prediction in Endoscopy

    Rau, Anita / Bhattarai, Binod / Agapito, Lourdes / Stoyanov, Danail

    2023  

    Abstract: Colorectal cancer remains one of the deadliest cancers in the world. In recent years computer-aided methods have aimed to enhance cancer screening and improve the quality and availability of colonoscopies by automatizing sub-tasks. One such task is ... ...

    Abstract Colorectal cancer remains one of the deadliest cancers in the world. In recent years computer-aided methods have aimed to enhance cancer screening and improve the quality and availability of colonoscopies by automatizing sub-tasks. One such task is predicting depth from monocular video frames, which can assist endoscopic navigation. As ground truth depth from standard in-vivo colonoscopy remains unobtainable due to hardware constraints, two approaches have aimed to circumvent the need for real training data: supervised methods trained on labeled synthetic data and self-supervised models trained on unlabeled real data. However, self-supervised methods depend on unreliable loss functions that struggle with edges, self-occlusion, and lighting inconsistency. Methods trained on synthetic data can provide accurate depth for synthetic geometries but do not use any geometric supervisory signal from real data and overfit to synthetic anatomies and properties. This work proposes a novel approach to leverage labeled synthetic and unlabeled real data. While previous domain adaptation methods indiscriminately enforce the distributions of both input data modalities to coincide, we focus on the end task, depth prediction, and translate only essential information between the input domains. Our approach results in more resilient and accurate depth maps of real colonoscopy sequences.

    Comment: First Data Engineering in Medical Imaging Workshop at MICCAI 2023
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006
    Publishing date 2023-10-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Federated Active Learning for Target Domain Generalisation

    Caramalau, Razvan / Bhattarai, Binod / Stoyanov, Danail

    2023  

    Abstract: In this paper, we introduce Active Learning framework in Federated Learning for Target Domain Generalisation, harnessing the strength from both learning paradigms. Our framework, FEDALV, composed of Active Learning (AL) and Federated Domain ... ...

    Abstract In this paper, we introduce Active Learning framework in Federated Learning for Target Domain Generalisation, harnessing the strength from both learning paradigms. Our framework, FEDALV, composed of Active Learning (AL) and Federated Domain Generalisation (FDG), enables generalisation of an image classification model trained from limited source domain client's data without sharing images to an unseen target domain. To this end, our FDG, FEDA, consists of two optimisation updates during training, one at the client and another at the server level. For the client, the introduced losses aim to reduce feature complexity and condition alignment, while in the server, the regularisation limits free energy biases between source and target obtained by the global model. The remaining component of FEDAL is AL with variable budgets, which queries the server to retrieve and sample the most informative local data for the targeted client. We performed multiple experiments on FDG w/ and w/o AL and compared with both conventional FDG baselines and Federated Active Learning baselines. Our extensive quantitative experiments demonstrate the superiority of our method in accuracy and efficiency compared to the multiple contemporary methods. FEDALV manages to obtain the performance of the full training target accuracy while sampling as little as 5% of the source client's data.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004 ; 006
    Publishing date 2023-12-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: TextAug

    Fawakherji, Mulham / Vazquez, Eduard / Giampa, Pasquale / Bhattarai, Binod

    Test time Text Augmentation for Multimodal Person Re-identification

    2023  

    Abstract: Multimodal Person Reidentification is gaining popularity in the research community due to its effectiveness compared to counter-part unimodal frameworks. However, the bottleneck for multimodal deep learning is the need for a large volume of multimodal ... ...

    Abstract Multimodal Person Reidentification is gaining popularity in the research community due to its effectiveness compared to counter-part unimodal frameworks. However, the bottleneck for multimodal deep learning is the need for a large volume of multimodal training examples. Data augmentation techniques such as cropping, flipping, rotation, etc. are often employed in the image domain to improve the generalization of deep learning models. Augmenting in other modalities than images, such as text, is challenging and requires significant computational resources and external data sources. In this study, we investigate the effectiveness of two computer vision data augmentation techniques: cutout and cutmix, for text augmentation in multi-modal person re-identification. Our approach merges these two augmentation strategies into one strategy called CutMixOut which involves randomly removing words or sub-phrases from a sentence (Cutout) and blending parts of two or more sentences to create diverse examples (CutMix) with a certain probability assigned to each operation. This augmentation was implemented at inference time without any prior training. Our results demonstrate that the proposed technique is simple and effective in improving the performance on multiple multimodal person re-identification benchmarks.

    Comment: 10 pages, 5 figures
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2023-12-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Histogram of Oriented Gradients meet deep learning: A novel multi-task deep network for 2D surgical image semantic segmentation.

    Bhattarai, Binod / Subedi, Ronast / Gaire, Rebati Raman / Vazquez, Eduard / Stoyanov, Danail

    Medical image analysis

    2023  Volume 85, Page(s) 102747

    Abstract: We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary tasks. Contrary to it, we propose to generate the pseudo-labels of an ... ...

    Abstract We present our novel deep multi-task learning method for medical image segmentation. Existing multi-task methods demand ground truth annotations for both the primary and auxiliary tasks. Contrary to it, we propose to generate the pseudo-labels of an auxiliary task in an unsupervised manner. To generate the pseudo-labels, we leverage Histogram of Oriented Gradients (HOGs), one of the most widely used and powerful hand-crafted features for detection. Together with the ground truth semantic segmentation masks for the primary task and pseudo-labels for the auxiliary task, we learn the parameters of the deep network to minimize the loss of both the primary task and the auxiliary task jointly. We employed our method on two powerful and widely used semantic segmentation networks: UNet and U2Net to train in a multi-task setup. To validate our hypothesis, we performed experiments on two different medical image segmentation data sets. From the extensive quantitative and qualitative results, we observe that our method consistently improves the performance compared to the counter-part method. Moreover, our method is the winner of FetReg Endovis Sub-challenge on Semantic Segmentation organised in conjunction with MICCAI 2021. Code and implementation details are available at:https://github.com/thetna/medical_image_segmentation.
    MeSH term(s) Humans ; Deep Learning ; Semantics ; Hand ; Image Processing, Computer-Assisted
    Language English
    Publishing date 2023-01-13
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2023.102747
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5206: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (2.OG)
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