Search Results for author: Milan Sonka

Found 19 papers, 6 papers with code

Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation

no code implementations19 Dec 2023 Fahim Ahmed Zaman, Mathews Jacob, Amanda Chang, Kan Liu, Milan Sonka, Xiaodong Wu

Diffusion models have shown impressive performance for image generation, often times outperforming other generative models.

Image Denoising Image Generation +4

U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation

2 code implementations29 Nov 2023 Yaopeng Peng, Milan Sonka, Danny Z. Chen

We evaluate our method on several public medical image segmentation datasets for skin lesion segmentation and polyp segmentation, and the experimental results demonstrate the segmentation accuracy of our new method over state-of-the-art methods, while preserving memory and computational efficiency.

Computational Efficiency Image Segmentation +4

PHG-Net: Persistent Homology Guided Medical Image Classification

1 code implementation28 Nov 2023 Yaopeng Peng, Hongxiao Wang, Milan Sonka, Danny Z. Chen

The PH module is lightweight and capable of integrating topological features into any CNN or Transformer architectures in an end-to-end fashion.

Image Classification Medical Image Classification

Trust, but Verify: Robust Image Segmentation using Deep Learning

no code implementations25 Oct 2023 Fahim Ahmed Zaman, Xiaodong Wu, Weiyu Xu, Milan Sonka, Raghuraman Mudumbai

We describe a method for verifying the output of a deep neural network for medical image segmentation that is robust to several classes of random as well as worst-case perturbations i. e. adversarial attacks.

Image Segmentation Medical Image Segmentation +2

Provable Multi-instance Deep AUC Maximization with Stochastic Pooling

1 code implementation14 May 2023 Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang

This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e. g., multiple 2D slices of a CT scan for a patient).

Stochastic Optimization

Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

4 code implementations ICCV 2021 Zhuoning Yuan, Yan Yan, Milan Sonka, Tianbao Yang

Our studies demonstrate that the proposed DAM method improves the performance of optimizing cross-entropy loss by a large margin, and also achieves better performance than optimizing the existing AUC square loss on these medical image classification tasks.

General Classification Graph Property Prediction +3

COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Network

no code implementations10 Sep 2020 Shakib Yazdani, Shervin Minaee, Rahele Kafieh, Narges Saeedizadeh, Milan Sonka

We also provide a visualization of the attention maps of the model for several test images, and show that our model is attending to the infected regions as intended.

Computed Tomography (CT) Specificity

COVID TV-UNet: Segmenting COVID-19 Chest CT Images Using Connectivity Imposed U-Net

1 code implementation24 Jul 2020 Narges Saeedizadeh, Shervin Minaee, Rahele Kafieh, Shakib Yazdani, Milan Sonka

Through experimental results on a relatively large-scale CT segmentation dataset of around 900 images, we show that adding this new regularization term leads to 2\% gain on overall segmentation performance compared to the U-Net model.

Computed Tomography (CT) Segmentation

Fully Automated 3D Segmentation of MR-Imaged Calf Muscle Compartments: Neighborhood Relationship Enhanced Fully Convolutional Network

no code implementations21 Jun 2020 Zhihui Guo, Honghai Zhang, Zhi Chen, Ellen van der Plas, Laurie Gutmann, Daniel Thedens, Peggy Nopoulos, Milan Sonka

Automated segmentation of individual calf muscle compartments from 3D magnetic resonance (MR) images is essential for developing quantitative biomarkers for muscular disease progression and its prediction.

Edge Detection Image Segmentation +1

DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction

no code implementations25 Mar 2019 Zhihui Guo, Junjie Bai, Yi Lu, Xin Wang, Kunlin Cao, Qi Song, Milan Sonka, Youbing Yin

The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask.

Object Semantic Segmentation

Just-Enough Interaction Approach to Knee MRI Segmentation: Data from the Osteoarthritis Initiative

no code implementations10 Mar 2019 Satyananda Kashyap, Honghai Zhang, Milan Sonka

State-of-the-art automated segmentation algorithms are not 100\% accurate especially when segmenting difficult to interpret datasets like those with severe osteoarthritis (OA).

MRI segmentation Segmentation

Learning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative

no code implementations10 Mar 2019 Satyananda Kashyap, Honghai Zhang, Karan Rao, Milan Sonka

4D LOGISMOS validation on 108 MRIs from baseline and 12 month follow-up scans of 54 patients showed a significant reduction in segmentation errors (\emph{p}$<$0. 01) compared to 3D.

Image Segmentation MRI segmentation +2

Efficient optimization for Hierarchically-structured Interacting Segments (HINTS)

no code implementations CVPR 2017 Hossam Isack, Olga Veksler, Ipek Oguz, Milan Sonka, Yuri Boykov

We propose an effective optimization algorithm for a general hierarchical segmentation model with geometric interactions between segments.

Segmentation

Hedgehog Shape Priors for Multi-Object Segmentation

no code implementations CVPR 2016 Hossam Isack, Olga Veksler, Milan Sonka, Yuri Boykov

In contrast to star-convexity, the tightness of our normal constraint can be changed giving better control over allowed shapes.

Descriptive Object +2

Thickness Mapping of Eleven Retinal Layers in Normal Eyes Using Spectral Domain Optical Coherence Tomography

no code implementations11 Dec 2013 Raheleh Kafieh, Hossein Rabbani, Fedra Hajizadeh, Michael D. Abramoff, Milan Sonka

This study was conducted to determine the thickness map of eleven retinal layers in normal subjects by spectral domain optical coherence tomography (SD-OCT) and evaluate their association with sex and age.

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