46 research outputs found
MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network
The inability to interpret the model prediction in semantically and visually
meaningful ways is a well-known shortcoming of most existing computer-aided
diagnosis methods. In this paper, we propose MDNet to establish a direct
multimodal mapping between medical images and diagnostic reports that can read
images, generate diagnostic reports, retrieve images by symptom descriptions,
and visualize attention, to provide justifications of the network diagnosis
process. MDNet includes an image model and a language model. The image model is
proposed to enhance multi-scale feature ensembles and utilization efficiency.
The language model, integrated with our improved attention mechanism, aims to
read and explore discriminative image feature descriptions from reports to
learn a direct mapping from sentence words to image pixels. The overall network
is trained end-to-end by using our developed optimization strategy. Based on a
pathology bladder cancer images and its diagnostic reports (BCIDR) dataset, we
conduct sufficient experiments to demonstrate that MDNet outperforms
comparative baselines. The proposed image model obtains state-of-the-art
performance on two CIFAR datasets as well.Comment: CVPR2017 Ora
Novel image markers for non-small cell lung cancer classification and survival prediction
BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients.
RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated.
CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers
Novel Image Markers for Non-Small Cell Lung Cancer Classification and Survival Prediction
BACKGROUND: Non-small cell lung cancer (NSCLC), the most common type of lung cancer, is one of serious diseases causing death for both men and women. Computer-aided diagnosis and survival prediction of NSCLC, is of great importance in providing assistance to diagnosis and personalize therapy planning for lung cancer patients.
RESULTS: In this paper we have proposed an integrated framework for NSCLC computer-aided diagnosis and survival analysis using novel image markers. The entire biomedical imaging informatics framework consists of cell detection, segmentation, classification, discovery of image markers, and survival analysis. A robust seed detection-guided cell segmentation algorithm is proposed to accurately segment each individual cell in digital images. Based on cell segmentation results, a set of extensive cellular morphological features are extracted using efficient feature descriptors. Next, eight different classification techniques that can handle high-dimensional data have been evaluated and then compared for computer-aided diagnosis. The results show that the random forest and adaboost offer the best classification performance for NSCLC. Finally, a Cox proportional hazards model is fitted by component-wise likelihood based boosting. Significant image markers have been discovered using the bootstrap analysis and the survival prediction performance of the model is also evaluated.
CONCLUSIONS: The proposed model have been applied to a lung cancer dataset that contains 122 cases with complete clinical information. The classification performance exhibits high correlations between the discovered image markers and the subtypes of NSCLC. The survival analysis demonstrates strong prediction power of the statistical model built from the discovered image markers
Push the Boundary of SAM: A Pseudo-label Correction Framework for Medical Segmentation
Segment anything model (SAM) has emerged as the leading approach for
zero-shot learning in segmentation, offering the advantage of avoiding
pixel-wise annotation. It is particularly appealing in medical image
segmentation where annotation is laborious and expertise-demanding. However,
the direct application of SAM often yields inferior results compared to
conventional fully supervised segmentation networks. While using SAM generated
pseudo label could also benefit the training of fully supervised segmentation,
the performance is limited by the quality of pseudo labels. In this paper, we
propose a novel label corruption to push the boundary of SAM-based
segmentation. Our model utilizes a novel noise detection module to distinguish
between noisy labels from clean labels. This enables us to correct the noisy
labels using an uncertainty-based self-correction module, thereby enriching the
clean training set. Finally, we retrain the network with updated labels to
optimize its weights for future predictions. One key advantage of our model is
its ability to train deep networks using SAM-generated pseudo labels without
relying on a subset of expert-level annotations. We demonstrate the
effectiveness of our proposed model on both X-ray and lung CT datasets,
indicating its ability to improve segmentation accuracy and outperform baseline
methods in label correction
Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract
Squamous Cell Carcinoma (SCC) is the most common cancer type of the
epithelium and is often detected at a late stage. Besides invasive diagnosis of
SCC by means of biopsy and histo-pathologic assessment, Confocal Laser
Endomicroscopy (CLE) has emerged as noninvasive method that was successfully
used to diagnose SCC in vivo. For interpretation of CLE images, however,
extensive training is required, which limits its applicability and use in
clinical practice of the method. To aid diagnosis of SCC in a broader scope,
automatic detection methods have been proposed. This work compares two methods
with regard to their applicability in a transfer learning sense, i.e. training
on one tissue type (from one clinical team) and applying the learnt
classification system to another entity (different anatomy, different clinical
team). Besides a previously proposed, patch-based method based on convolutional
neural networks, a novel classification method on image level (based on a
pre-trained Inception V.3 network with dedicated preprocessing and
interpretation of class activation maps) is proposed and evaluated. The newly
presented approach improves recognition performance, yielding accuracies of
91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The
generalization from oral cavity to the second data set (vocal folds) lead to
similar area-under-the-ROC curve values than a direct training on the vocal
folds data set, indicating good generalization.Comment: Erratum for version 1, correcting the number of CLE image sequences
used in one data se