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16th international conference on Brain Informatics, BI 2023

作者： Jahan, Sobhana Saif Adib, Md. Rawnak Mahmud, Mufti Kaiser, M. Shamim Department of Computer Science and Engineering Bangladesh University of Professionals Dhaka Bangladesh Department of Information and Communication Technology Bangladesh University of Professionals Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Department of Computer Science Nottingham Trent University Clifton Lane NottinghamNG11 8NS United Kingdom Institute of Information and Technology Jahangirnagar University Dhaka Bangladesh

ISBN: (纸本)9783031430749

Alzheimer’s disease (AD) is a common form of dementia that affects brain regions that control cognition, memory, and language. Globally, more than 55 million people are suffering from dementia. Given these troubling statistics, predicting AD is critical for future medications and treatment. Mild Cognitive Impairment (MCI) is a vital stage for patients because from this stage a majority of patients turn into AD patients. Early MCI (EMCI) and Late MCI (LMCI) are vital two stages of MCI. Successful prediction of Cognitive Normal (CN), AD, MCI, EMCI, and LMCI stages is a big challenge. Although there are models for predicting these stages but all models are not near to accurate. One major concern for these lacking is not having sufficient datasets to train the model. Data augmentation can be a solution to create an abundance of MRI data. One major issue regarding Machine Learning (ML) is the black box nature. Due to this limitation, user satisfaction as well as trust in the model’s prediction is missing. Explainable Artificial Intelligence (XAI) is the torchbearer approach and through this, the reason behind every decision can be observed by the user. this paper proposes a comparison between four XAI models named Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, Score-weighted CAM (Score-CAM), and Faster Score-CAM. For performing the five classes (CN, AD, EMCI, MCI, and LMCI) prediction, EfficientNet models are used because these models have performed well on the augmented dataset. Among EfficientNet models (B0-B7), EfficientNetB7 has performed the best. the testing accuracy and loss are 96.34% and 0.12, respectively. After that, the last layer of the EfficientNetB7 model is passed to the XAI models. Comparing the four XAI models it is observed that Grad-CAM++ and Score-CAM are better performing than others. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Super-Resolution MRH Reconstruction for Mouse Models 16th

Super-Resolution MRH Reconstruction for Mouse Models

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16th international conference on Brain Informatics, BI 2023

作者： Ha, Juhyung Wang, Nian Maharjan, Surendra Zhang, Xuhong Computer Science Department Luddy School of Informatics University of Indiana Bloomington47408 United States Department of Radiology and Imaging Sciences Indiana University Indianapolis46202 United States Stark Neurosciences Research Institute Indiana University Indianapolis46202 United States

ISBN: (纸本)9783031430749

Magnetic Resonance Imaging (MRI) has been widely used in pathology research and plays a vital role in clinical diagnoses. However, obtaining high-resolution MRI stays a significant challenge as the process is restrained by the hardware capacity, sampling time, signal-to-noise ratio (SNR), and subject comfort. Compared to MRI, Magnetic Resonance Histology (MRH) is more costly as it has a higher spatial resolution. Deep learning models have recently succeeded in MRI Super-Resolution (SR) reconstruction from Low-Resolution (LR) images. However, it is still hard to overcome artifacts during the restoration process. the models usually have limitations in the high-frequency component representations, which can reflect finer details as in MRH images. In this work, we propose an imaging restoration framework that can conduct SR reconstruction for the whole mouse brain MRH images, and the model is based on a deep dense network. We first synthesized LR images from HR ones to train the proposed model, and then we quantitatively compared the model performance with existing methods proposed for the human brain and natural images. Finally, we demonstrated the superior visual reconstruction and interpretation of the proposed model compared to prior works. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： image reconstruction

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Predicting Individual Differences from Brain Responses to Music: A Comparison of Functional Connectivity Measures 16th

Predicting Individual Differences from Brain Responses to ...

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16th international conference on Brain Informatics, BI 2023

作者： Jain, Arihant Toiviainen, Petri Alluri, Vinoo International Institute of Information Technology Hyderabad Hyderabad India Finnish Centre for Interdisciplinary Music Research Department of Music Art and Culture Studies University of Jyvaskyla Jyväskylä Finland

ISBN: (纸本)9783031430749

Individual differences are known to modulate brain responses to music. Recent neuroscience research suggests that each individual has unique and fundamentally stable functional brain connections irrespective of task. Our study aims to identify individual differences such as gender and musical expertise from brain responses to music. thirty-six participants’ functional Magnetic Resonance Imaging (fMRI) responses were measured while listening to three 8-min-long musical pieces representing different musical styles. they were analyzed using various temporal and spectral functional connectivity (FC) measures. these FC measures were compared to identify the one that captured the most variation associated with gender and musical expertise. Subsequently, the measures were used to classify distinct population groupings using a binary Support Vector Machine (SVM). Our results revealed that Coherence, a spectral-domain FC measure, captured the maximum variation for musical stimuli. However, in classifying individuals based on gender and musical expertise, a composite measure outperformed any single measure and had above-chance accuracy. this suggests that each FC measure captures different aspects of the relationship between brain regions and is influenced by the level of analysis (group or individual) and the task, such as similarity analysis or classification. Further ramifications of the findings are discussed. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Music

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the 19th international conference on medical image computing and computer-assisted intervention (MICCAI 2016)

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medical image ANALYSIS 2017年 41卷 1-1页

作者： Ourselin, Sebastien Sabuncu, Mert R. Wells, William Joskowicz, Leo Unal, Gozde Maier, Andreas Univ Erlangen Nurnberg Dept Comp Sci Erlangen Germany

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025年 15188 LNCS卷 v页

作者： Antony, Bhavna Lee, Cecilia S. Chen, Hao Fu, Huazhu Fang, Huihui Zheng, Yalin Federation University Australia Mount HelenVIC Australia University of Washington SeattleWA United States Hong Kong University of Science and Technology Kowloon Hong Kong Institute of High Performance Computing Singapore Singapore Pazhou Lab. Guangzhou China

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the rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification 23

The rise of AI language pathologists: exploring two-level pr...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Linhao Qu Xiaoyuan Luo Kexue Fu Manning Wang Zhijian Song Digital Medical Research Center School of Basic Medical Science Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention

this paper introduces the novel concept of few-shot weakly supervised learning for pathology Whole Slide image (WSI) classification, denoted as FSWC. A solution is proposed based on prompt learning and the utilization of a large language model, GPT-4. Since a WSI is too large and needs to be divided into patches for processing, WSI classification is commonly approached as a Multiple Instance Learning (MIL) problem. In this context, each WSI is considered a bag, and the obtained patches are treated as instances. the objective of FSWC is to classify both bags and instances with only a limited number of labeled bags. Unlike conventional few-shot learning problems, FSWC poses additional challenges due to its weak bag labels within the MIL framework. Drawing inspiration from the recent achievements of vision-language models (V-L models) in downstream few-shot classification tasks, we propose a two-level prompt learning MIL framework tailored for pathology, incorporating language prior knowledge. Specifically, we leverage CLIP to extract instance features for each patch, and introduce a prompt-guided pooling strategy to aggregate these instance features into a bag feature. Subsequently, we employ a small number of labeled bags to facilitate few-shot prompt learning based on the bag features. Our approach incorporates the utilization of GPT-4 in a question-and-answer mode to obtain language prior knowledge at both the instance and bag levels, which are then integrated into the instance and bag level language prompts. Additionally, a learnable component of the language prompts is trained using the available few-shot labeled data. We conduct extensive experiments on three real WSI datasets encompassing breast cancer, lung cancer, and cervical cancer, demonstrating the notable performance of the proposed method in bag and instance classification. Codes will be available at https://***/miccaiif/TOP.

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LogTrans: Providing Efficient Local-Global Fusion with Transformer and CNN Parallel Network for Biomedical image Segmentation

LogTrans: Providing Efficient Local-Global Fusion with Trans...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Xingqing Nie Xiaogen Zhou Zhiqiang Li Luoyan Wang Xingtao Lin Tong Tong College of Physics and Information Engineering Fuzhou University Fuzhou China Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China

Accurate biomedical image segmentation is a prerequisite for excellent computer-aided diagnosis (CAD) systems. A series of researches have shown that convolutional neural networks (CNNs) have made impressive progress in segmentation tasks. Nevertheless, owing to the finite receptive field of CNN-based algorithms, such networks are focused too much on local area features rather than global context. While the Transformer architecture can encode global dependencies information through the self-attention mechanism, this mechanism typically ignores the local pixel-level structural information within each divided patch. therefore, a better solution is still needed for how to integrate CNN architecture with Transformer architecture efficiently. In this essay, we propose an originative parallel segmentation algorithm called LogTrans. First, in the encoder path, the local details and global contour dependencies on the entire image are captured by the CNN branch and the Transformer branch, respectively. then these two branches complement each other by a novel separate-combiner (SeCo) module, leading to better fused features. Moreover, we attempt to further enhance the segmentation properties by using a residual stackable dilated (ReS $D$ ) block, which applies residual shortcut connections to resolve dimension alterations in the target region and stacks dilated convolutions to capture more spatial information. the proposed LogTrans framework was evaluated on two biomedical datasets, including ISIC-2017 and UITNS-2022 datasets. Collectively, multiple results have indicated that our LogTrans performs superior with other state-of-the-art architectures in both visual comparison and quantitative appraisal.

关键词： image segmentation Visualization Fuses Transformers Decoding image restoration Convolutional neural networks

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High Speed and Efficient Reconfigurable Histogram Equalisation Architecture for image Contrast Enhancement 8th

High Speed and Efficient Reconfigurable Histogram Equalisati...

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8th international conference on Information, Communication and computing Technology, ICICCT 2023

作者： Agalya, P. Hanumantharaju, M.C. Department of Electronics and Communication Engineering Sapthagiri College of Engineering Bangalore India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore India

ISBN: (纸本)9783031438370

Histogram equalisation is a point processing technique used to improve image quality in wide variety of applications including real-time video surveillance, medical imaging, industrial automation, intelligent self-navigation systems, and oceanography. this research paper proposes a unique, simple high-speed reconfigurable architecture for the implementation of histogram equalisation on field programmable gate arrays (FPGA). the proposed histogram equalisation architecture comprises of a pixel mapping unit and a histogram computing unit. the histogram computation unit makes use of counters and comparators to compute the cumulative histogram of images. the pixel mapping unit comprise of a delay control unit with a register array to map the output pixel intensity value for each input pixel grey level by adopting a straightforward and effective pixel transformation method. the proposed architecture has been developed using register transfer level (RTL) - compliant Verilog HDL code, simulated using Xilinx's integrated simulator (ISim), synthesized and implemented on a Kintex7 family of Field Programmable Gate Array (FPGA) device. Simulation and synthesis results demonstrate that the proposed architecture can be implemented with a processing time of 2.353 ns and a maximum frequency of operation of 425 MHz. Experimental results shows that the proposed method outperforms other existing methods. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pixels

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Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-assisted K-D Trees 8

Approximate Nearest-Neighbour Fields via Massively-Parallel ...

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8th IEEE international conference on Big Data (Big Data)

作者： Oancea, Cosmin Eugen Robroek, Ties Gieseke, Fabian Univ Copenhagen Dept Comp Sci DIKU Copenhagen Denmark Univ Munster Munster Germany

ISBN: (纸本)9781728162515

Nearest neighbour fields accurately and intuitively describe the transformation between two images and have been heavily used in computer vision. Generating such fields, however, is not an easy task due to the induced computational complexity, which quickly grows with the sizes of the images. Modern parallel devices such as graphics processing units depict a viable way of reducing the practical run time of such compute-intensive tasks. In this work, we propose a novel parallel implementation for one of the state-of-the-art methods for the computation of nearest neighbour fields, called propagation-assisted k-d trees. the resulting implementation yields valuable computational savings over a corresponding multi-core implementation. Additionally, it is tuned to consume only little additional memory and is, hence, capable of dealing with high-resolution image data, which is vital as image quality standards keep rising.

关键词： nearest neighbour fields propagation-assisted k-d trees high-performance computing computer vision

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Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data 2nd

Progression Free Survival Prediction for Head and Neck Cance...

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th international conference on medical image computing and computer-assisted intervention, MICCAI 2021

作者： Naser, Mohamed A. Wahid, Kareem A. Mohamed, Abdallah S. R. Abdelaal, Moamen Abobakr He, Renjie Dede, Cem van Dijk, Lisanne V. Fuller, Clifton D. Department of Radiation Oncology The University of Texas MD Anderson Cancer HoustonTX77030 United States

ISBN: (纸本)9783030982522

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT images and clinical data as separate input channels to predict PFS in days. Internal model validation based on 10-fold cross-validation using the training data (N = 224) yielded C-index values up to 0.622 (without) and 0.842 (with) censoring status considered in C-index computation, respectively. We then implemented model ensembling approaches based on the training data cross-validation folds to predict the PFS of the test set patients (N = 101). External validation on the test set for the best ensembling method yielded a C-index value of 0.694, placing 2nd in the competition. Our results are a promising example of how deep learning approaches can effectively utilize imaging and clinical data for medical outcome prediction in HNSCC, but further work in optimizing these processes is needed. © 2022, Springer Nature Switzerland AG.

关键词： image enhancement

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