no code implementations • 16 Apr 2024 • Kechun Liu, Wenjun Wu, Joann G. Elmore, Linda G. Shapiro
Accurate cancer diagnosis remains a critical challenge in digital pathology, largely due to the gigapixel size and complex spatial relationships present in whole slide images.
1 code implementation • 27 Jun 2023 • Kalyani Marathe, Mahtab Bigverdi, Nishat Khan, Tuhin Kundu, Patrick Howe, Sharan Ranjit S, Anand Bhattad, Aniruddha Kembhavi, Linda G. Shapiro, Ranjay Krishna
We train multiple models with different masked image modeling objectives to showcase the following findings: Representations trained on our automatically generated MIMIC-3M outperform those learned from expensive crowdsourced datasets (ImageNet-1K) and those learned from synthetic environments (MULTIVIEW-HABITAT) on two dense geometric tasks: depth estimation on NYUv2 (1. 7%), and surface normals estimation on Taskonomy (2. 05%).
1 code implementation • 11 Dec 2020 • Beibin Li, Ezgi Mercan, Sachin Mehta, Stevan Knezevich, Corey W. Arnold, Donald L. Weaver, Joann G. Elmore, Linda G. Shapiro
In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification.
no code implementations • 13 Sep 2018 • Shu Liang, Xiufeng Huang, Xianyu Meng, Kunyao Chen, Linda G. Shapiro, Ira Kemelmacher-Shlizerman
In this paper, we describe a system that can completely automatically create a reconstruction from any video (even a selfie video), and we don't require specific views, since taking your -90 degree, 90 degree, and full back views is not feasible in a selfie capture.
no code implementations • 13 Sep 2018 • Shu Liang, Linda G. Shapiro, Ira Kemelmacher-Shlizerman
Our method is to gradually "grow" the head mesh starting from the frontal face and extending to the rest of views using photometric stereo constraints.
no code implementations • 13 Sep 2018 • Shu Liang, Ira Kemelmacher-Shlizerman, Linda G. Shapiro
We further combine the input depth frame with the matched database shapes into a single mesh that results in a high-resolution shape of the input person.
no code implementations • NeurIPS 2012 • Shulin Yang, Liefeng Bo, Jue Wang, Linda G. Shapiro
It differs from recognition of basic categories, such as humans, tables, and computers, in that there are global similarities in shape or structure shared within a category, and the differences are in the details of the object parts.