Evaluations of Deep Learning Methods for Pathology Image Classification

Huang, Shiang Suo; Yu, Cai-Rong; Liao, Yi-Sheng; Huang, Chun-Rong

doi:10.1109/biocas54905.2022.9948661

Cited by 7 publications

(1 citation statement)

References 20 publications

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“…Although supervised deep learning methods [19] , [20] , [21] , [22] , [23] , [24] have achieved amazing performance for lesion classification, detection or segmentation in the medical domain, time-consuming annotated labels are required. To reduce the labeling burdens of the physicians, weakly-supervised methods are proposed to achieve computer-aided diagnosis for CT scans based on weakly annotated labels.…”

Section: Related Workmentioning

confidence: 99%

Multiple Field-of-View Based Attention Driven Network for Weakly Supervised Common Bile Duct Stone Detection

Chang

Lin

Hsieh

et al. 2023

IEEE J. Transl. Eng. Health Med.

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Objective: Common bile duct (CBD) stones caused diseases are life-threatening. Because CBD stones locate in the distal part of the CBD and have relatively small sizes, detecting CBD stones from CT scans is a challenging issue in the medical domain. Methods and procedures: We propose a deep learning based weakly-supervised method called multiple field-of-view based attention driven network (MFADNet) to detect CBD stones from CT scans based on image-level labels. Three dominant modules including a multiple field-of-view encoder, an attention driven decoder and a classification network are collaborated in the network. The encoder learns the feature of multi-scale contextual information while the decoder with the classification network is applied to locate the CBD stones based on spatial-channel attentions. To drive the learning of the whole network in a weakly-supervised and end-to-end trainable manner, four losses including the foreground loss, background loss, consistency loss and classification loss are proposed. Results: Compared with state-of-the-art weakly-supervised methods in the experiments, the proposed method can accurately classify and locate CBD stones based on the quantitative and qualitative results. Conclusion: We propose a novel multiple field-of-view based attention driven network for a new medical application of CBD stone detection from CT scans while only image-levels are required to reduce the burdens of labeling and help physicians automatically diagnose CBD stones. The source code is available at after acceptance. Clinical impact: Our deep learning method can help physicians localize relatively small CBD stones for effectively diagnosing CBD stone caused diseases.

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