Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM

Root Identification in Minirhizotron Imagery with Multiple Instance Learning

In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron imagery. However, labeling roots for training data (or otherwise) is an extremely tedious and time-consuming task. This paper aims to address this problem by labeling data at the image level (rather than…

Real-Time Human Motion Capture with Multiple Depth Cameras

Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts withou…

CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation

Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To automatically segment the cancerous regions, fully supervised segmentation algorithms require labor-intensive and time-consuming labeling at the pixel level. In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image-level labels. Using multiple instance learning (MIL)-based label enrichment, CAMEL splits the image into latticed instances and automatically generates instance-level labels. After label enrichment, the instance-level labe…

一种面向多摄像机视角监控视频异常检测的多实例学习方法

A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views

本文提出MC-MIL算法，结合多实例学习与多摄像机视角，解决监控视频异常检测中的标签缺失、遮挡和杂乱问题，在PETS-2009数据集上显著提升F1分数。

CAMIL: 用于全切片图像中癌症检测与分型的上下文感知多示例学习

CAMIL: Context-Aware Multiple Instance Learning for Cancer Detection and Subtyping in Whole Slide Images

针对现有全切片图像分析的多示例学习模型忽略上下文导致分类错误的问题，提出融合邻域约束注意力与上下文先验的CAMIL架构，在三个癌症诊断基准上取得优于前沿方法的AUC，且提升了模型可解释性。

Adapting Grad-CAM for Embedding Networks

The gradient-weighted class activation mapping (Grad-CAM) method can faithfully highlight important regions in images for deep model prediction in image classification, image captioning and many other tasks. It uses the gradients in back-propagation as weights (grad-weights) to explain network decisions. However, applying Grad-CAM to embedding networks raises significant challenges because embedding networks are trained by millions of dynamically paired examples (e.g. triplets). To overcome these challenges, we propose an adaptation of the Grad-CAM method for embedding networks. First, we aggr…