no code implementations • 5 Feb 2024 • Suraj Mishra, Danny Z. Chen
Medical image segmentation using deep neural networks has been highly successful.
no code implementations • 4 Sep 2022 • Suraj Mishra, Yizhe Zhang, Li Zhang, Tianyu Zhang, X. Sharon Hu, Danny Z. Chen
Specifically, we analyze the convolutional network's behavior (field-of-view) to find the location of deep supervision for improved feature extraction.
1 code implementation • 1 Jul 2022 • Yizhe Zhang, Suraj Mishra, Peixian Liang, Hao Zheng, Danny Z. Chen
We aim to quantitatively measure the practical usability of medical image segmentation models: to what extent, how often, and on which samples a model's predictions can be used/trusted.
no code implementations • IEEE Transactions on Medical Imaging 2022 • Suraj Mishra, Yizhe Zhang, Danny Z. Chen, X. Sharon Hu
In this paper, we study medical image segmentation by focusing on robust data-specific feature extraction to achieve improved dense prediction.
no code implementations • 6 Jul 2021 • Suraj Mishra, Danny Z. Chen, X. Sharon Hu
Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network.
no code implementations • 17 Apr 2021 • Suraj Mishra, Danny Z. Chen, X. Sharon Hu
In this paper, we study retinal vessel segmentation by incorporating tiny vessel segmentation into our framework for the overall accurate vessel segmentation.
1 code implementation • 6 Jan 2019 • Suraj Mishra, Peixian Liang, Adam Czajka, Danny Z. Chen, X. Sharon Hu
Convolutional neural networks (CNNs) for biomedical image analysis are often of very large size, resulting in high memory requirement and high latency of operations.