4 citations · 6 across the 7 of their papers we have counts for
7 papers
SAM-MIL: A Spatial Contextual Aware Multiple Instance Learning Approach for Whole Slide Image Classification
Heng Fang, Sheng Huang, Wenhao Tang +2
Multiple Instance Learning (MIL) represents the predominant framework in Whole Slide Image (WSI) classification, covering aspects such as sub-typing, diagnosis, and beyond. Current…
Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology
Wenhao Tang, Fengtao Zhou, Sheng Huang +3
Multiple instance learning (MIL) is the most widely used framework in computational pathology, encompassing sub-typing, diagnosis, prognosis, and more. However, the existing MIL pa…
Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classification
Wenhao Tang, Sheng Huang, Xiaoxian Zhang +3
The whole slide image (WSI) classification is often formulated as a multiple instance learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WS…
Deformable Kernel Expansion Model for Efficient Arbitrary-shaped Scene Text Detection
Tao He, Sheng Huang, Wenhao Tang +1
Scene text detection is a challenging computer vision task due to the high variation in text shapes and ratios. In this work, we propose a scene text detector named Deformable Kern…
PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress Classification
Wenhao Tang, Sheng Huang, Xiaoxian Zhang +1
Automatic pavement distress classification facilitates improving the efficiency of pavement maintenance and reducing the cost of labor and resources. A recently influential branch…
Weakly Supervised Patch Label Inference Networks for Efficient Pavement Distress Detection and Recognition in the Wild
Sheng Huang, Wenhao Tang, Guixin Huang +2
Automatic image-based pavement distress detection and recognition are vital for pavement maintenance and management. However, existing deep learning-based methods largely omit the…
An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection
Wenhao Tang, Sheng Huang, Qiming Zhao +2
We present a novel deep learning framework named the Iteratively Optimized Patch Label Inference Network (IOPLIN) for automatically detecting various pavement distresses that are n…