6 papers
Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation
Suraj Mishra, Danny Z. Chen
Medical image segmentation using deep neural networks has been highly successful. However, the effectiveness of these networks is often limited by inadequate dense prediction and i…
Data-Driven Deep Supervision for Skin Lesion Classification
Suraj Mishra, Yizhe Zhang, Li Zhang +3
Automatic classification of pigmented, non-pigmented, and depigmented non-melanocytic skin lesions have garnered lots of attention in recent years. However, imaging variations in s…
Usable Region Estimate for Assessing Practical Usability of Medical Image Segmentation Models
Yizhe Zhang, Suraj Mishra, Peixian Liang +2
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/tru…
Image Complexity Guided Network Compression for Biomedical Image Segmentation
Suraj Mishra, Danny Z. Chen, X. Sharon Hu
Compression is a standard procedure for making convolutional neural networks (CNNs) adhere to some specific computing resource constraints. However, searching for a compressed arch…
Objective-Dependent Uncertainty Driven Retinal Vessel Segmentation
Suraj Mishra, Danny Z. Chen, X. Sharon Hu
From diagnosing neovascular diseases to detecting white matter lesions, accurate tiny vessel segmentation in fundus images is critical. Promising results for accurate vessel segmen…
CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation
Suraj Mishra, Peixian Liang, Adam Czajka +2
Convolutional neural networks (CNNs) for biomedical image analysis are often of very large size, resulting in high memory requirement and high latency of operations. Searching for…