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4th international Workshop on Simulation and Synthesis in medical Imaging, SASHIMI 2019, held in conjunction with the 22nd international conference on medical image computing and computer assisted intervention, MICCAI 2019

4th International Workshop on Simulation and Synthesis in Me...

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4th international Workshop on Simulation and Synthesis in medical Imaging, SASHIMI 2019, held in conjunction with the 22nd international conference on medical image computing and computer assisted intervention, MICCAI 2019

ISBN: (纸本)9783030327774

the proceedings contain 16 papers. the special focus in this conference is on international Workshop on Simulation and Synthesis in medical Imaging, held in conjunction with the international conference on medical image computing and computer assisted intervention. the topics include: Physics-informed brain MRI segmentation;3D medical image synthesis by factorised representation and deformable model learning;cycle-consistent training for reducing negative jacobian determinant in deep registration networks;ISMORE: An iterative self super-resolution algorithm;an optical model of whole blood for detecting platelets in lens-free images;Evaluation of the realism of an MRI simulator for stroke lesion prediction using convolutional neural network;Improved MR to CT synthesis for pet/mr attenuation correction using imitation learning;Unpaired multi-contrast MR image synthesis using generative adversarial networks;unsupervised retina image synthesis via disentangled representation learning;Pseudo-normal PET synthesis with generative adversarial networks for localising hypometabolism in epilepsies;breast mass detection in mammograms via blending adversarial learning;Tunable CT lung nodule synthesis conditioned on background image and semantic features;mask2Lesion: Mask-constrained adversarial skin lesion image synthesis;towards annotation-free segmentation of fluorescently labeled cell membranes in confocal microscopy images.

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2nd international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2019, and the 9th international Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd international conference on medical Imaging and computer-assisted intervention, MICCAI 2019

2nd International Workshop on Interpretability of Machine In...

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2nd international Workshop on Interpretability of Machine Intelligence in medical image computing, iMIMIC 2019, and the 9th international Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held in conjunction with the 22nd international conference on medical Imaging and computer-assisted intervention, MICCAI 2019

ISBN: (纸本)9783030338497

the proceedings contain 10 papers. the special focus in this conference is on Interpretability of Machine Intelligence in medical image computing. the topics include: Automated enriched medical concept generation for chest X-ray images;UBS: A dimension-agnostic metric for concept vector interpretability applied to radiomics;generation of multimodal justification using visual word constraint model for explainable computer-aided diagnosis;Incorporating task-specific structural knowledge into CNNs for brain midline shift detection;guideline-based additive explanation for computer-aided diagnosis of lung nodules;deep neural network or dermatologist?;towards interpretability of segmentation networks by analyzing deepDreams;towards automatic diagnosis from multi-modal medical data;deep learning based multi-modal registration for retinal imaging.

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6th international Workshop on Ophthalmic medical image Analysis, OMIA 2019, held in conjunction with the 22nd international conference on medical Imaging computing and computer-assisted intervention, MICCAI 2019

6th International Workshop on Ophthalmic Medical Image Analy...

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6th international Workshop on Ophthalmic medical image Analysis, OMIA 2019, held in conjunction with the 22nd international conference on medical Imaging computing and computer-assisted intervention, MICCAI 2019

ISBN: (纸本)9783030329556

the proceedings contain 22 papers. the special focus in this conference is on international Workshop on Ophthalmic medical image Analysis, held in conjunction with the international conference on medical Imaging computing and computer-assisted intervention. the topics include: Aggressive Posterior Retinopathy of Prematurity Automated Diagnosis via a Deep Convolutional Network;structure-Aware Noise Reduction Generative Adversarial Network for Optical Coherence Tomography image;Fovea Localization in Fundus Photographs by Faster R-CNN with Physiological Prior;multi-discriminator Generative Adversarial Networks for Improved thin Retinal Vessel Segmentation;encoder-Decoder Attention Network for Lesion Segmentation of Diabetic Retinopathy;An Improved MPB-CNN Segmentation Method for Edema Area and Neurosensory Retinal Detachment in SD-OCT images;Network Pruning for OCT image Classification;fundus image Based Retinal Vessel Segmentation Utilizing a Fast and Accurate Fully Convolutional Network;the Channel Attention Based Context Encoder Network for Inner Limiting Membrane Detection;robust Optic Disc Localization by Large Scale Learning;u-Net with Spatial Pyramid Pooling for Drusen Segmentation in Optical Coherence Tomography;deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal images;preface;Dictionary Learning Informed Deep Neural Network with Application to OCT images;Shape Decomposition of Foveal Pit Morphology Using Scan Geometry Corrected OCT;semi-supervised Adversarial Learning for Diabetic Retinopathy Screening;3D-CNN for Glaucoma Detection Using Optical Coherence Tomography;guided M-Net for High-Resolution Biomedical image Segmentation with Weak Boundaries;foveal Avascular Zone Segmentation in Clinical Routine Fluorescein Angiographies Using Multitask Learning;An Amplified-Target Loss Approach for Photoreceptor Layer Segmentation in Pathological OCT Scans.

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10th international Workshop on Machine Learning in medical Imaging, MLMI 2019 held in conjunction with the 22nd international conference on medical image computing and computer-assisted intervention, MICCAI 2019

10th International Workshop on Machine Learning in Medical I...

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10th international Workshop on Machine Learning in medical Imaging, MLMI 2019 held in conjunction with the 22nd international conference on medical image computing and computer-assisted intervention, MICCAI 2019

ISBN: (纸本)9783030326913

the proceedings contain 78 papers. the special focus in this conference is on international Workshop on Machine Learning in medical Imaging, held in conjunction with the international conference on medical image computing and computer-assisted intervention. the topics include: GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for Automatic Detection of Esophageal Abnormalities in Endoscopic images;deep Active Lesion Segmentation;infant Brain Deformable Registration Using Global and Local Label-Driven Deep Regression Learning;a Relation Hashing Network Embedded with Prior Features for Skin Lesion Classification;end-to-End Adversarial Shape Learning for Abdomen Organ Deep Segmentation;privacy-Preserving Federated Brain Tumour Segmentation;residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Histology images;semi-supervised Multi-task Learning with Chest X-Ray images;Novel Bi-directional images Synthesis Based on WGAN-GP with GMM-Based Noise Generation;spatial Regularized Classification Network for Spinal Dislocation Diagnosis;pseudo-labeled Bootstrapping and Multi-stage Transfer Learning for the Classification and Localization of Dysplasia in Barrett’s Esophagus;Anatomy-Aware Self-supervised Fetal MRI Synthesis from Unpaired Ultrasound images;end-to-End Boundary Aware Networks for medical image Segmentation;Automatic Rodent Brain MRI Lesion Segmentation with Fully Convolutional Networks;Morphological Simplification of Brain MR images by Deep Learning for Facilitating Deformable Registration;Joint Shape Representation and Classification for Detecting PDAC;FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans;ultrasound Liver Fibrosis Diagnosis Using Multi-indicator Guided Deep Neural Networks;weakly Supervised Segmentation by a Deep Geodesic Prior;correspondence-Steered Volumetric Descriptor Learning Using Deep Functional Maps;globally-Aware Multiple Instance Classifier for Breast Ca

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4th international MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2018

4th International MICCAI Brainlesion Workshop, BrainLes 2018...

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4th international MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2018

ISBN: (纸本)9783030117252

the proceedings contain 92 papers. the special focus in this conference is on MICCAI Brainlesion. the topics include: Segmentation of post-operative glioblastoma in MRI by U-net with patient-specific interactive refinement;Characterizing peritumoral tissue using DTI-based free water elimination;Deep 2D encoder-decoder convolutional neural network for multiple sclerosis lesion segmentation in brain MRI;shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis;detection of midline brain abnormalities using convolutional neural networks;Deep autoencoding models for unsupervised anomaly segmentation in brain MR images;Brain tumor detection and classification from multi-sequence MRI: study using convnets;Voxel-wise comparison with a-contrario analysis for automated segmentation of multiple sclerosis lesions from multimodal MRI;a graph based similarity measure for assessing altered connectivity in traumatic brain injury;CT brain perfusion: A clinical perspective;multi-scale convolutional-stack aggregation for robust white matter hyperintensities segmentation;holistic brain tumor screening and classification based on densenet and recurrent neural network;3D texture feature learning for noninvasive estimation of gliomas pathological subtype;pathology segmentation using distributional differences to images of healthy origin;multi-stage association analysis of glioblastoma gene expressions with texture and spatial patterns;Stroke lesion segmentation with 2D novel CNN pipeline and novel loss function;Contra-lateral information CNN for core lesion segmentation based on native CTP in acute stroke;dense multi-path u-net for ischemic stroke lesion segmentation in multiple image modalities;Multi-scale deep convolutional neural network for stroke lesions segmentation on CT images;ischemic stroke lesion segmentation using adversarial learning.

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4th international MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2018

4th International MICCAI Brainlesion Workshop, BrainLes 2018...

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4th international MICCAI Brainlesion Workshop, BrainLes 2018 held in conjunction with the medical image computing for computer assisted intervention conference, MICCAI 2018

ISBN: (纸本)9783030117221

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SplitFed Resilience to Packet Loss: Where to Split, that is the Question 26th

SplitFed Resilience to Packet Loss: Where to Split, that is ...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Shiranthika, Chamani Kafshgari, Zahra Hafezi Saeedi, Parvaneh Bajic, Ivan V. Simon Fraser Univ Sch Engn Sci Burnaby BC Canada

ISBN: (纸本)9783031474002;9783031474019

Decentralized machine learning has broadened its scope recently with the invention of Federated Learning (FL), Split Learning (SL), and their hybrids like Split Federated Learning (SplitFed or SFL). the goal of SFL is to reduce the computational power required by each client in FL and parallelize SL while maintaining privacy. this paper investigates the robustness of SFL against packet loss on communication links. the performance of various SFL aggregation strategies is examined by splitting the model at two points - shallow split and deep split - and testing whether the split point makes a statistically significant difference to the accuracy of the final model. Experiments are carried out on a segmentation model for human embryo images and indicate the statistically significant advantage of a deeper split point.

关键词： SplitFed Learning packet loss human embryo image segmentation

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Concept Bottleneck with Visual Concept Filtering for Explainable medical image Classification 26th

Concept Bottleneck with Visual Concept Filtering for Explain...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Kim, Injae Kim, Jongha Choi, Joonmyung Kim, Hyunwoo J. Korea Univ Dept Comp Sci & Engn Seoul South Korea

ISBN: (纸本)9783031474002;9783031474019

Interpretability is a crucial factor in building reliable models for various medical applications. Concept Bottleneck Models (CBMs) enable interpretable image classification by utilizing human-understandable concepts as intermediate targets. Unlike conventional methods that require extensive human labor to construct the concept set, recent works leveraging Large Language Models (LLMs) for generating concepts made automatic concept generation possible. However, those methods do not consider whether a concept is visually relevant or not, which is an important factor in computing meaningful concept scores. therefore, we propose a visual activation score that measures whether the concept contains visual cues or not, which can be easily computed with unlabeled image data. Computed visual activation scores are then used to filter out the less visible concepts, thus resulting in a final concept set with visually meaningful concepts. Our experimental results show that adopting the proposed visual activation score for concept filtering consistently boosts performance compared to the baseline. Moreover, qualitative analyses also validate that visually relevant concepts are successfully selected with the visual activation score.

关键词： medical image Classification Explainable AI Concept Bottleneck Models Large Language Models

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Histopathological image Analysis with Style-Augmented Feature Domain Mixing for Improved Generalization 26th

Histopathological Image Analysis with Style-Augmented Featur...

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Khamankar, Vaibhav Bera, Sutanu Bhattacharya, Saumik Sen, Debashis Biswas, Prabir Kumar Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur India

ISBN: (纸本)9783031474002;9783031474019

Histopathological images are essential for medical diagnosis and treatment planning, but interpreting them accurately using machine learning can be challenging due to variations in tissue preparation, staining and imaging protocols. Domain generalization aims to address such limitations by enabling the learning models to generalize to new datasets or populations. Style transfer-based data augmentation is an emerging technique that can be used to improve the generalizability of machine learning models for histopathological images. However, existing style transfer-based methods can be computationally expensive, and they rely on artistic styles, which may negatively impact model accuracy. In this study, we propose a feature domain style mixing technique that uses adaptive instance normalization to estimate style-mixed versions of image features. We compare our proposed method with existing style transfer-based data augmentation methods and found that it performs similarly or better, despite requiring lower computation. Our results demonstrate the potential of feature domain statistics mixing in the generalization of learning models for histopathological image analysis.

关键词： Domain Shift Domain Generalization Mitotic Figure Style Mixing Feature Domain Augmentation Histopathological image

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GPC: Generative and General Pathology image Classifier 26th

GPC: Generative and General Pathology Image Classifier

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26th international conference on medical image computing and computer-assisted intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Nguyen, Anh Tien Kwak, Jin Tae Korea Univ Sch Elect Engn Seoul 02841 South Korea

ISBN: (纸本)9783031474002;9783031474019

Deep learning has been increasingly incorporated into various computational pathology applications to improve its efficiency, accuracy, and robustness. Although successful, most previous approaches for image classification have crucial drawbacks. there exist numerous tasks in pathology, but one needs to build a model per task, i.e., a task-specific model, thereby increasing the number of models, training resources, and cost. Moreover, transferring arbitrary task-specific model to another task is still a challenging problem. Herein, we propose a task-agnostic generative and general pathology image classifier, so called GPC, that aims at learning from diverse kinds of pathology images and conducting numerous classification tasks in a unified model. GPC, equipped with a convolutional neural network and a Transformer-based language model, maps pathology images into a high-dimensional feature space and generates pertinent class labels as texts via the image-to-text classification mechanism. We evaluate GPC on six datasets for four different pathology image classification tasks. Experimental results show that GPC holds considerable potential for developing an effective and efficient universal model for pathology image analysis.

关键词： Computational pathology image classification Generative model image-to-Text

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