Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs
The lack of fine-grained annotations hinders the deployment of automated diagnosis systems, which require human-interpretable justification for their decision process. In this paper, we address the problem of weakly supervised identification and localization of abnormalities in chest radiographs. To that end, we introduce a novel loss function for training convolutional neural networks increasing the \emph{localization confidence} and assisting the overall \emph{disease identification}. The loss leverages both image- and patch-level predictions to generate auxiliary supervision. Rather than forming strictly binary from the predictions as done in previous loss formulations, we create targets in a more customized manner, which allows the loss to account for possible misclassification. We show that the supervision provided within the proposed learning scheme leads to better performance and more precise predictions on prevalent datasets for multiple-instance learning as well as on the NIH~ChestX-Ray14 benchmark for disease recognition than previously used losses.
Code (1)
Tasks
Multiple Instance LearningSimilar Papers 제목 키워드 기반
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection
Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this wo…
Anomaly DetectionAnomaly Detection In Surveillance VideosPseudo LabelWeakly-supervised Video Anomaly DetectionTransformer based multiple instance learning for weakly supervised histopathology image segmentation
Hispathological image segmentation algorithms play a critical role in computer aided diagnosis technology. The development of weakly supervised segmentation algorithm alleviates the problem of medical image annotation th…
Image SegmentationMultiple Instance LearningSegmentationSemantic Segmentation+2DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision
We introduce DiscoBox, a novel framework that jointly learns instance segmentation and semantic correspondence using bounding box supervision. Specifically, we propose a self-ensembling framework where instance segmentat…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic correspondence+1Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation
Learning semantic segmentation models under image-level supervision is far more challenging than under fully supervised setting. Without knowing the exact pixel-label correspondence, most weakly-supervised methods rely o…
SegmentationSemantic SegmentationWeakly supervised Semantic SegmentationWeakly-Supervised Semantic SegmentationModality-Aware Contrastive Instance Learning with Self-Distillation for Weakly-Supervised Audio-Visual Violence Detection
Weakly-supervised audio-visual violence detection aims to distinguish snippets containing multimodal violence events with video-level labels. Many prior works perform audio-visual integration and interaction in an early …
Anomaly Detection In Surveillance Videosaudio-visual learningMultiple Instance Learning